{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of clustering of node embeddings with a traditional community detection method\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/community_detection/attacks_clustering_analysis.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/community_detection/attacks_clustering_analysis.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this use case is to demonstrate how node embeddings from graph convolutional neural networks trained in unsupervised manner are comparable to standard community detection methods based on graph partitioning. \n",
    "\n",
    "Specifically, we use unsupervised [graphSAGE](http://snap.stanford.edu/graphsage/) approach to learn node embeddings of terrorist groups in a publicly available dataset of global terrorism events, and analyse clusters of these embeddings. We compare the clusters to communities produced by [Infomap](http://www.mapequation.org), a state-of-the-art approach of graph partitioning based on information theory. \n",
    "\n",
    "We argue that clustering based on unsupervised graphSAGE node embeddings allow for richer representation of the data than its graph partitioning counterpart as the former takes into account node features together with the graph structure, while the latter utilises only the graph structure. \n",
    "\n",
    "We demonstrate, using the terrorist group dataset, that the infomap communities and the graphSAGE embedding clusters (GSEC) provide qualitatively different insights into underlying data patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__The Global Terrorism Database (GTD)__  used in this demo is available here: https://www.kaggle.com/START-UMD/gtd. GTD is an open-source database including information on terrorist attacks around the world from 1970 through 2017. The GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 180,000 attacks. The database is maintained by researchers at the National Consortium for the Study of Terrorism and Responses to Terrorism (START), headquartered at the University of Maryland. For information refer to the initial data source: https://www.start.umd.edu/gtd/.\n",
    "\n",
    "Full dataset contains information on more than 180,000 Terrorist Attacks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Glossary:\n",
    "For this particular study we adopt the following terminology:\n",
    "\n",
    "- __a community__ is a group of nodes produced by a traditional community detection algorithm (infomap community in this use case)\n",
    "- __a cluster__ is a group of nodes that were clustered together using a clustering algorithm applied to node embeddings (here, [DBSCAN clustering](https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf) applied to unsupervised GraphSAGE embeddings). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more detailed explanation of unsupervised graphSAGE see [Unsupervised graphSAGE demo](../embeddings/graphsage-unsupervised-sampler-embeddings.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of the demo is structured as follows. First, we load the data and preprocess it (see `utils.py` for detailed steps of network and features generation). Then we apply infomap and visualise results of one selected community obtained from this method. Next, we apply unsupervised graphSAGE on the same data and extract node embeddings. We first tune DBSCAN hyperparameters, and apply DBSCAN that produces sufficient numbers of clusters with minimal number of noise points. We look at the resulting clusters, and investigate a single selected cluster in terms of the graph structure and features of the nodes in this cluster. Finally, we conclude our investigation with the summary of the results.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import igraph as ig\n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') # supress warnings due to some future deprications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import stellargraph as sg\n",
    "from stellargraph.data import EdgeSplitter\n",
    "from stellargraph.mapper import GraphSAGELinkGenerator, GraphSAGENodeGenerator\n",
    "from stellargraph.layer import GraphSAGE, link_classification\n",
    "from stellargraph.data import UniformRandomWalk\n",
    "from stellargraph.data import UnsupervisedSampler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "from stellargraph import globalvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplleaflet\n",
    "from itertools import count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the raw data of terrorist attacks that we use as a starting point of our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_raw = pd.read_csv(\n",
    "    \"~/data/globalterrorismdb_0718dist.csv\",\n",
    "    sep=\",\",\n",
    "    engine=\"python\",\n",
    "    encoding=\"ISO-8859-1\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading preprocessed features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting feature set is the aggregation of features for each of the terrorist groups (`gname`). We collect such features as total number of attacks per terrorist group, number of perpetrators etc. We use `targettype` and `attacktype` (number of attacks/targets of particular type) and transform it to a wide representation of data (e.g each type is a separate column with the counts for each group). Refer to `utils.py` for more detailed steps of the preprocessing and data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnames_features = utils.load_features(input_data=dt_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gname</th>\n",
       "      <th>n_attacks</th>\n",
       "      <th>n_nperp</th>\n",
       "      <th>n_nkil</th>\n",
       "      <th>n_nwound</th>\n",
       "      <th>success_ratio</th>\n",
       "      <th>Armed Assault</th>\n",
       "      <th>Assassination</th>\n",
       "      <th>Bombing/Explosion</th>\n",
       "      <th>Facility/Infrastructure Attack</th>\n",
       "      <th>...</th>\n",
       "      <th>Other</th>\n",
       "      <th>Police</th>\n",
       "      <th>Private Citizens &amp; Property</th>\n",
       "      <th>Religious Figures/Institutions</th>\n",
       "      <th>Telecommunication</th>\n",
       "      <th>Terrorists/Non-State Militia</th>\n",
       "      <th>Tourists</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Violent Political Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1 May</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>14 K Triad</td>\n",
       "      <td>4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>14 March Coalition</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14th of December Command</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15th of September Liberation Legion</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 gname  n_attacks  n_nperp  n_nkil  n_nwound  \\\n",
       "0                                1 May         10      3.0     2.0       0.0   \n",
       "1                           14 K Triad          4     24.0     0.0       0.0   \n",
       "2                   14 March Coalition          1      0.0     5.0      80.0   \n",
       "3             14th of December Command          3      0.0     0.0       0.0   \n",
       "4  15th of September Liberation Legion          1      9.0     0.0       1.0   \n",
       "\n",
       "   success_ratio  Armed Assault  Assassination  Bombing/Explosion  \\\n",
       "0            0.9            0.0            4.0                6.0   \n",
       "1            1.0            0.0            0.0                4.0   \n",
       "2            1.0            1.0            0.0                0.0   \n",
       "3            1.0            0.0            0.0                3.0   \n",
       "4            1.0            0.0            0.0                1.0   \n",
       "\n",
       "   Facility/Infrastructure Attack  ...  Other  Police  \\\n",
       "0                             0.0  ...    0.0     1.0   \n",
       "1                             0.0  ...    0.0     0.0   \n",
       "2                             0.0  ...    0.0     0.0   \n",
       "3                             0.0  ...    0.0     0.0   \n",
       "4                             0.0  ...    0.0     0.0   \n",
       "\n",
       "   Private Citizens & Property  Religious Figures/Institutions  \\\n",
       "0                          0.0                             0.0   \n",
       "1                          3.0                             0.0   \n",
       "2                          0.0                             0.0   \n",
       "3                          0.0                             2.0   \n",
       "4                          0.0                             0.0   \n",
       "\n",
       "   Telecommunication  Terrorists/Non-State Militia  Tourists  Transportation  \\\n",
       "0                0.0                           0.0       0.0             0.0   \n",
       "1                0.0                           0.0       0.0             0.0   \n",
       "2                0.0                           1.0       0.0             0.0   \n",
       "3                0.0                           0.0       0.0             0.0   \n",
       "4                0.0                           0.0       0.0             0.0   \n",
       "\n",
       "   Utilities  Violent Political Party  \n",
       "0        0.0                      0.0  \n",
       "1        0.0                      0.0  \n",
       "2        0.0                      0.0  \n",
       "3        0.0                      0.0  \n",
       "4        0.0                      0.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnames_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Edgelist creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw dataset contains information for the terrotist attacks. It contains information about some incidents being related. However, the resulting graph is very sparse. Therefore, we create our own schema for the graph, where two terrorist groups are connected, if they had at least one terrorist attack in the same country and in the same decade. To this end we proceed as follows:\n",
    "\n",
    "-  we group the event data  by `gname` - the names of the terrorist organisations.\n",
    "-  we create a new feature `decade` based on `iyear`, where one bin consists of 10 years (attacks of 70s, 80s, and so on). \n",
    "-  we add the concatination of of the decade and the country of the attack: `country_decade` which will become a link between two terrorist groups.\n",
    "-  finally, we create an edgelist, where two terrorist groups are linked if they have operated in the same country in the same decade. Edges are undirected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, some edges are created based on the column `related` in the raw event data. The description of the data does not go into details how these incidents were related.  We utilise this information creating a link for terrorist groups if the terrorist attacks performed by these groups were related. If related events corresponded to the same terrorist group, they were discarded (we don't use self-loops in the graph). However, the majority of such links are already covered by `country_decade` edge type. Refer to `utils.py` for more information on graph creation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = utils.load_network(input_data=dt_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 3490\n",
      "Number of edges: 106903\n",
      "Average degree:  61.2625\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connected components of the network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the graph is disconnected, consisting of 21 connected components. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "print(nx.number_connected_components(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the sizes of the connected components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3435, 7, 6, 5, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n"
     ]
    }
   ],
   "source": [
    "Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)\n",
    "cc_sizes = []\n",
    "for cc in list(Gcc):\n",
    "    cc_sizes.append(len(cc.nodes()))\n",
    "print(cc_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of connected components' sizes shows that there is a single large component, and a few isolated groups. We expect the community detection/node embedding clustering algorithms discussed below to discover non-trivial communities that are not simply the connected components of the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traditional community detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perform traditional community detection via `infomap` implemented in `igraph`. We translate the original `networkx` graph object to `igraph`, apply `infomap` to it to detect communities, and assign the resulting community memberships back to the `networkx` graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'IGRAPH U--- 3490 106903 -- '"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# translate the object into igraph\n",
    "g_ig = ig.Graph.Adjacency(\n",
    "    (nx.to_numpy_matrix(G) > 0).tolist(), mode=ig.ADJ_UNDIRECTED\n",
    ")  # convert via adjacency matrix\n",
    "g_ig.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering with 3490 elements and 160 clusters\n"
     ]
    }
   ],
   "source": [
    "# perform community detection\n",
    "random.seed(123)\n",
    "c_infomap = g_ig.community_infomap()\n",
    "print(c_infomap.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get 160 communities, meaning that the largest connected components of the graph are partitioned into more granular groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 160 artists>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the community sizes\n",
    "infomap_sizes = c_infomap.sizes()\n",
    "plt.title(\"Infomap community sizes\")\n",
    "plt.xlabel(\"community id\")\n",
    "plt.ylabel(\"number of nodes\")\n",
    "plt.bar(list(range(1, len(infomap_sizes) + 1)), infomap_sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6287979581321445"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modularity metric for infomap\n",
    "c_infomap.modularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The discovered infomap communities have smooth distribution of cluster sizes, which indicates that the underlying graph structure has a natural partitioning. The modularity score is also pretty high indicating that nodes are more tightly connected within clusters than expected from random graph, i.e., the discovered communities are tightly-knit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign community membership results back to networkx, keep the dictionary for later comparisons with the clustering\n",
    "infomap_com_dict = dict(zip(list(G.nodes()), c_infomap.membership))\n",
    "nx.set_node_attributes(G, infomap_com_dict, \"c_infomap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisation of the infomap communities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualise the resulting communities using maps as the constructed network is based partially on the geolocation. The raw data have a lat-lon coordinates for each of the attacks. Terrorist groups might perform attacks in different locations. However, currently the simplified assumption is made, and the average of latitude and longitude for each terrorist group is calculated. Note that it might result in some positions being \"off\", but it is good enough to provide a glimpse whether the communities are consistent with the location of that groups. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3490, 2)\n",
      "3490\n"
     ]
    }
   ],
   "source": [
    "# fill NA based on the country name\n",
    "dt_raw.latitude[\n",
    "    dt_raw[\"gname\"] == \"19th of July Christian Resistance Brigade\"\n",
    "] = 12.136389\n",
    "dt_raw.longitude[\n",
    "    dt_raw[\"gname\"] == \"19th of July Christian Resistance Brigade\"\n",
    "] = -86.251389\n",
    "\n",
    "# filter only groups that are present in a graph\n",
    "dt_filtered = dt_raw[dt_raw[\"gname\"].isin(list(G.nodes()))]\n",
    "\n",
    "# collect averages of latitude and longitude for each of gnames\n",
    "avg_coords = dt_filtered.groupby(\"gname\")[[\"latitude\", \"longitude\"]].mean()\n",
    "\n",
    "print(avg_coords.shape)\n",
    "print(len(G.nodes()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As plotting the whole graph is not feasible, we investigate a single community"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Specify community id in the range of infomap total number of clusters__ (`len(infomap_sizes)`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "com_id = 50  # smaller number - larger community, as it's sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 18\n",
      "Number of edges: 103\n",
      "Average degree:  11.4444\n"
     ]
    }
   ],
   "source": [
    "# extraction of a subgraph from the nodes in this community\n",
    "com_G = G.subgraph(\n",
    "    [n for n, attrdict in G.nodes.items() if attrdict[\"c_infomap\"] == com_id]\n",
    ")\n",
    "print(nx.info(com_G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot community structure only\n",
    "pos = nx.random_layout(com_G, seed=123)\n",
    "plt.figure(figsize=(10, 8))\n",
    "nx.draw_networkx(com_G, pos, edge_color=\"#26282b\", node_color=\"blue\", alpha=0.3)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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if (gjData.features.length != 0) {
  var gj = L.geoJson(gjData, {
    style: function (feature) {
      return feature.properties;
    },
    pointToLayer: function (feature, latlng) {
      var icon = L.divIcon({'html': feature.properties.html, 
        iconAnchor: [feature.properties.anchor_x, 
                     feature.properties.anchor_y], 
          className: 'empty'});  // What can I do about empty?
      return L.marker(latlng, {icon: icon});
    }
  });
  gj.addTo(map);
  
  map.fitBounds(gj.getBounds());
} else {
  map.setView([0, 0], 1);
}
</script>
</body>\" width=\"100%\" height=\"360\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot on the map\n",
    "nodes = com_G.nodes()\n",
    "com_avg_coords = avg_coords[avg_coords.index.isin(list(nodes))]\n",
    "com_avg_coords.fillna(\n",
    "    com_avg_coords.mean(), inplace=True\n",
    ")  # fill missing values with the average\n",
    "new_order = [1, 0]\n",
    "com_avg_coords = com_avg_coords[com_avg_coords.columns[new_order]]\n",
    "pos = com_avg_coords.T.to_dict(\"list\")  # layout is based on the provided coordindates\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "nx.draw_networkx_edges(com_G, pos, edge_color=\"grey\")\n",
    "nx.draw_networkx_nodes(\n",
    "    com_G, pos, nodelist=nodes, with_labels=True, node_size=200, alpha=0.5\n",
    ")\n",
    "nx.draw_networkx_labels(com_G, pos, font_color=\"#362626\", font_size=50)\n",
    "mplleaflet.display(fig=ax.figure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(**N.B.:** the above interactive plot will only appear after running the cell, and is not rendered in GitHub!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of results based on infomap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Infomap is a robust community detection algorithm, and shows good results that are in line with the expectations. Most of the communities are tightly connected and reflect the geographical position of the events of the terrorist groups. That is because the graph schema is expressed as \n",
    "> _two terrorist groups are connected if they have terrorist events in the same country in the same decade_. \n",
    "\n",
    "However, no node features are taken into account in the case of the traditional community detection.\n",
    "\n",
    "Next, we explore the GSEC approach, where node features are used along with the graph structure. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node represenatation learning with unsupervised graphSAGE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we apply unsupervised GraphSAGE that takes into account node features as well as graph structure, to produce *node embeddings*. In our case, similarity of node embeddings depicts similarity of the terrorist groups in terms of their operations, targets and attack types (node features) as well as in terms of time and place of attacks (graph structure)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3490, 34)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we reload the graph to get rid of assigned attributes\n",
    "G = utils.load_network(input_data=dt_raw)  # to make sure that graph is clean\n",
    "\n",
    "# filter features to contain only gnames that are among nodes of the network\n",
    "filtered_features = gnames_features[gnames_features[\"gname\"].isin(list(G.nodes()))]\n",
    "filtered_features.set_index(\"gname\", inplace=True)\n",
    "filtered_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_attacks</th>\n",
       "      <th>n_nperp</th>\n",
       "      <th>n_nkil</th>\n",
       "      <th>n_nwound</th>\n",
       "      <th>success_ratio</th>\n",
       "      <th>Armed Assault</th>\n",
       "      <th>Assassination</th>\n",
       "      <th>Bombing/Explosion</th>\n",
       "      <th>Facility/Infrastructure Attack</th>\n",
       "      <th>Hijacking</th>\n",
       "      <th>...</th>\n",
       "      <th>Other</th>\n",
       "      <th>Police</th>\n",
       "      <th>Private Citizens &amp; Property</th>\n",
       "      <th>Religious Figures/Institutions</th>\n",
       "      <th>Telecommunication</th>\n",
       "      <th>Terrorists/Non-State Militia</th>\n",
       "      <th>Tourists</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Violent Political Party</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1 May</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14 March Coalition</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14th of December Command</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15th of September Liberation Legion</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16 January Organization for the Liberation of Tripoli</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    n_attacks  n_nperp  \\\n",
       "gname                                                                    \n",
       "1 May                                                      10      3.0   \n",
       "14 March Coalition                                          1      0.0   \n",
       "14th of December Command                                    3      0.0   \n",
       "15th of September Liberation Legion                         1      9.0   \n",
       "16 January Organization for the Liberation of T...         24      0.0   \n",
       "\n",
       "                                                    n_nkil  n_nwound  \\\n",
       "gname                                                                  \n",
       "1 May                                                  2.0       0.0   \n",
       "14 March Coalition                                     5.0      80.0   \n",
       "14th of December Command                               0.0       0.0   \n",
       "15th of September Liberation Legion                    0.0       1.0   \n",
       "16 January Organization for the Liberation of T...     1.0      32.0   \n",
       "\n",
       "                                                    success_ratio  \\\n",
       "gname                                                               \n",
       "1 May                                                         0.9   \n",
       "14 March Coalition                                            1.0   \n",
       "14th of December Command                                      1.0   \n",
       "15th of September Liberation Legion                           1.0   \n",
       "16 January Organization for the Liberation of T...            1.0   \n",
       "\n",
       "                                                    Armed Assault  \\\n",
       "gname                                                               \n",
       "1 May                                                         0.0   \n",
       "14 March Coalition                                            1.0   \n",
       "14th of December Command                                      0.0   \n",
       "15th of September Liberation Legion                           0.0   \n",
       "16 January Organization for the Liberation of T...           20.0   \n",
       "\n",
       "                                                    Assassination  \\\n",
       "gname                                                               \n",
       "1 May                                                         4.0   \n",
       "14 March Coalition                                            0.0   \n",
       "14th of December Command                                      0.0   \n",
       "15th of September Liberation Legion                           0.0   \n",
       "16 January Organization for the Liberation of T...            0.0   \n",
       "\n",
       "                                                    Bombing/Explosion  \\\n",
       "gname                                                                   \n",
       "1 May                                                             6.0   \n",
       "14 March Coalition                                                0.0   \n",
       "14th of December Command                                          3.0   \n",
       "15th of September Liberation Legion                               1.0   \n",
       "16 January Organization for the Liberation of T...                4.0   \n",
       "\n",
       "                                                    Facility/Infrastructure Attack  \\\n",
       "gname                                                                                \n",
       "1 May                                                                          0.0   \n",
       "14 March Coalition                                                             0.0   \n",
       "14th of December Command                                                       0.0   \n",
       "15th of September Liberation Legion                                            0.0   \n",
       "16 January Organization for the Liberation of T...                             0.0   \n",
       "\n",
       "                                                    Hijacking  ...  Other  \\\n",
       "gname                                                          ...          \n",
       "1 May                                                     0.0  ...    0.0   \n",
       "14 March Coalition                                        0.0  ...    0.0   \n",
       "14th of December Command                                  0.0  ...    0.0   \n",
       "15th of September Liberation Legion                       0.0  ...    0.0   \n",
       "16 January Organization for the Liberation of T...        0.0  ...    0.0   \n",
       "\n",
       "                                                    Police  \\\n",
       "gname                                                        \n",
       "1 May                                                  1.0   \n",
       "14 March Coalition                                     0.0   \n",
       "14th of December Command                               0.0   \n",
       "15th of September Liberation Legion                    0.0   \n",
       "16 January Organization for the Liberation of T...     0.0   \n",
       "\n",
       "                                                    Private Citizens & Property  \\\n",
       "gname                                                                             \n",
       "1 May                                                                       0.0   \n",
       "14 March Coalition                                                          0.0   \n",
       "14th of December Command                                                    0.0   \n",
       "15th of September Liberation Legion                                         0.0   \n",
       "16 January Organization for the Liberation of T...                          0.0   \n",
       "\n",
       "                                                    Religious Figures/Institutions  \\\n",
       "gname                                                                                \n",
       "1 May                                                                          0.0   \n",
       "14 March Coalition                                                             0.0   \n",
       "14th of December Command                                                       2.0   \n",
       "15th of September Liberation Legion                                            0.0   \n",
       "16 January Organization for the Liberation of T...                             0.0   \n",
       "\n",
       "                                                    Telecommunication  \\\n",
       "gname                                                                   \n",
       "1 May                                                             0.0   \n",
       "14 March Coalition                                                0.0   \n",
       "14th of December Command                                          0.0   \n",
       "15th of September Liberation Legion                               0.0   \n",
       "16 January Organization for the Liberation of T...                0.0   \n",
       "\n",
       "                                                    Terrorists/Non-State Militia  \\\n",
       "gname                                                                              \n",
       "1 May                                                                        0.0   \n",
       "14 March Coalition                                                           1.0   \n",
       "14th of December Command                                                     0.0   \n",
       "15th of September Liberation Legion                                          0.0   \n",
       "16 January Organization for the Liberation of T...                           0.0   \n",
       "\n",
       "                                                    Tourists  Transportation  \\\n",
       "gname                                                                          \n",
       "1 May                                                    0.0             0.0   \n",
       "14 March Coalition                                       0.0             0.0   \n",
       "14th of December Command                                 0.0             0.0   \n",
       "15th of September Liberation Legion                      0.0             0.0   \n",
       "16 January Organization for the Liberation of T...       0.0             0.0   \n",
       "\n",
       "                                                    Utilities  \\\n",
       "gname                                                           \n",
       "1 May                                                     0.0   \n",
       "14 March Coalition                                        0.0   \n",
       "14th of December Command                                  0.0   \n",
       "15th of September Liberation Legion                       0.0   \n",
       "16 January Organization for the Liberation of T...        0.0   \n",
       "\n",
       "                                                    Violent Political Party  \n",
       "gname                                                                        \n",
       "1 May                                                                   0.0  \n",
       "14 March Coalition                                                      0.0  \n",
       "14th of December Command                                                0.0  \n",
       "15th of September Liberation Legion                                     0.0  \n",
       "16 January Organization for the Liberation of T...                      0.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_features.head()  # take a glimpse at the feature data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perform a log-transform of the feature set to rescale feature values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transforming features to be on log scale\n",
    "node_features = filtered_features.transform(lambda x: np.log1p(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check that there are no misspelled gnames left\n",
    "set(list(G.nodes())) - set(list(node_features.index.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised graphSAGE\n",
    "\n",
    "(For a detailed unsupervised GraphSAGE workflow with a narrative, see [Unsupervised graphSAGE demo](../embeddings/graphsage-unsupervised-sampler-embeddings.ipynb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 3490, Edges: 106903\n",
      "\n",
      " Node types:\n",
      "  default: [3490]\n",
      "    Edge types: default-default->default\n",
      "\n",
      " Edge types:\n",
      "    default-default->default: [106903]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Gs = sg.StellarGraph.from_networkx(G, node_features=node_features)\n",
    "print(Gs.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameter specification\n",
    "number_of_walks = 3\n",
    "length = 5\n",
    "batch_size = 50\n",
    "epochs = 10\n",
    "num_samples = [20, 20]\n",
    "layer_sizes = [100, 100]\n",
    "learning_rate = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsupervisedSamples = UnsupervisedSampler(\n",
    "    Gs, nodes=G.nodes(), length=length, number_of_walks=number_of_walks\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = GraphSAGELinkGenerator(Gs, batch_size, num_samples)\n",
    "train_gen = generator.flow(unsupervisedSamples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(layer_sizes) == len(num_samples)\n",
    "\n",
    "graphsage = GraphSAGE(\n",
    "    layer_sizes=layer_sizes, generator=generator, bias=True, dropout=0.0, normalize=\"l2\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now build a Keras model from the GraphSAGE class that we can use for unsupervised predictions. We add a `link_classfication` layer as unsupervised training operates on node pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
     ]
    }
   ],
   "source": [
    "x_inp, x_out = graphsage.in_out_tensors()\n",
    "\n",
    "prediction = link_classification(\n",
    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method=\"ip\"\n",
    ")(x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(lr=learning_rate),\n",
    "    loss=keras.losses.binary_crossentropy,\n",
    "    metrics=[keras.metrics.binary_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1676/1676 - 602s - loss: 0.5891 - binary_accuracy: 0.7083\n",
      "Epoch 2/10\n",
      "1676/1676 - 631s - loss: 0.5779 - binary_accuracy: 0.7310\n",
      "Epoch 3/10\n",
      "1676/1676 - 618s - loss: 0.5753 - binary_accuracy: 0.7361\n",
      "Epoch 4/10\n",
      "1676/1676 - 619s - loss: 0.5742 - binary_accuracy: 0.7372\n",
      "Epoch 5/10\n",
      "1676/1676 - 604s - loss: 0.5740 - binary_accuracy: 0.7384\n",
      "Epoch 6/10\n",
      "1676/1676 - 587s - loss: 0.5736 - binary_accuracy: 0.7409\n",
      "Epoch 7/10\n",
      "1676/1676 - 573s - loss: 0.5725 - binary_accuracy: 0.7426\n",
      "Epoch 8/10\n",
      "1676/1676 - 573s - loss: 0.5707 - binary_accuracy: 0.7429\n",
      "Epoch 9/10\n",
      "1676/1676 - 575s - loss: 0.5740 - binary_accuracy: 0.7361\n",
      "Epoch 10/10\n",
      "1676/1676 - 586s - loss: 0.5719 - binary_accuracy: 0.7382\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_gen,\n",
    "    epochs=epochs,\n",
    "    verbose=2,\n",
    "    use_multiprocessing=False,\n",
    "    workers=1,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting node embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_ids = list(Gs.nodes())\n",
    "node_gen = GraphSAGENodeGenerator(Gs, batch_size, num_samples).flow(node_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = keras.Model(inputs=x_inp[::2], outputs=x_out[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/70 [==============================] - 10s 142ms/step\n"
     ]
    }
   ],
   "source": [
    "node_embeddings = embedding_model.predict(node_gen, workers=4, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2D t-sne plot of the resulting node embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we visually check whether embeddings have some underlying cluster structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3490, 100)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TSNE visualisation to check whether the embeddings have some structure:\n",
    "X = node_embeddings\n",
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2, random_state=123)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_ids)\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=node_ids)\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "\n",
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(emb_transformed[0], emb_transformed[1], alpha=alpha)\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\"{} visualization of GraphSAGE embeddings\".format(transform.__name__))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### t-sne colored by infomap\n",
    "We also depict the same t-sne plot colored by infomap communities. As we can observe t-sne of GraphSAGE embeddings do not really separate the infomap communities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 't-sne with colors corresponding to infomap communities')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E2ZPP0R6L0xSOcKBU4IxkhjPTGeKaSm0oQo2uszSWYImiYR06wkM/u2+hoxcQcFyCs3QCThsKhQL/+rd/R/hADy2hMNc3t7MknqIxHCGpaViu6910HY6QNQ10VSWthSg7DnFFpTkSRUFBUwSulKR0nYyuI1yXestmz5YXFjqKAQHHJVD4AacNWzZtwti9FyyblnCMtmgMwa+vD7KlpCEUxpEwbVvEFQ1TOkwYVVoiUQCEAF0oOLioQkFHRRcKVA0KoyOUSqWFjGJAwHEJFH7AaYFhGGx59HHqhYrpuNSHwigIFCGQQErTKdo2IVXBdB0sVxJRVaqOQ962CPl3tR9dRnb0VPFp20RXFVQBuC6meSK3AgYEnBoChR9wWlAqlRClEiFFQ1cUbBzKjo3AU+JtkRiO65I1DEKKgiNddKEwYRpMWwZl18GUEk0ILOmiCEHFsRmsllGFgikliWgMwwimqgIWL4HCDzgtEEKgqCqxaISwplGwHEaMCo6UKEBU01iVSjFsVMiaVeKahildQqiMmwZPTo5RtE2kxGv1WxZbcpMUbAsE2NEIHUuWoChBlQpYvASLhgNOC1KpFMmOduTQCOlYnHypRE+pSEiodEVjqEKwOpHmULnIQ+MjrEikiAqVrGuT0UJsz+co2TZ14TAAtutiS2iJRhlxXBJLOmnt7grW4QcsaoLSGXBaoKoq515+GQ9u205dfT0h1yVnWmzPT9JXKdIcjvjj8oI6Pcz9QwNkwmFq9RCKotAajjJhVFER1EbCxDWdmK4zZJuMJ2K867rrMOsy1NTULHRUAwLmJFD4AacN6844g31XXcG+e+7FyU8TU1SqlolpGPSWCmQNgynLpIpLeyZDORxiLB6lQQ/zzMAA3XoE1zYYLRqgKFQ0jSXr1/GnH/oQA9PTLN9wJkKIhY5mQMCcBAo/YFHgui779u1j0+NPMdw/QDyVZP05Gzn//POJx+PzMjauaRrveO97eaKlhZ/f8X32bn0RWa5SNUwcx8XVVMrxKImGBtafdy5vfsfbOeOccxgZHubZxx7j6Yd+iTKdp7WxiWVLl3L+ueciFUHvxAQ1a1axfMWKeUiJgIDXDiHl4jmv7Nxzz5XBFYenBxMTEzy3+Tl2PreFwwcPcXD3PpSJIjWWRtkyMS2TPBayLsbF11zBDb/7TtZuOJPa2tp5Cd+yLHp7e+nr62NkaIhwKISi63R1ddHa2kp9fT3RaPQl71QqFQ4dPMihnbtwq1VcJOnmFpavX0dbW1vQug9YMIQQW6SU576su0DhB5xKLMvil/f/nB9+478o7h8gYkLfxAgNRYWCNFAQtBEnoYQwhWREq5JLKpx39Rs4+5ILOPfNV7B02bIFjYOUEsuyUBQlmKQNWBS8UoUflNaAU4Zpmnz6zz7JU9//GeGiSaOMctjKUyPDTGLRSIx11BBBJ+nquECdW2Hn1BSbHn6c9tY2tjzwGOn3ZKirq1uweAghCIWCK5oDXn8ECj/glNDb28uf3PyHjG/eS8ySNMsI4AIu01QJ6QrdoTgokmmnwiFrGlM6tEXinK/X0O+Uee7B+znzwot4sjHFjTe9C9u2efzxx9ny5FOYlQp1ba1cd/31dHd3L3BsAwIWJ4HCD3hNkVLy7KbNfPOz/0T0uQEutOsoS4siFqOUmBYGHYk4y0NpEkKjnggqEFUVTOmSNnWa1DgqgiPTVdITOZ7+0d1E0ynu+ea3qCmUOKO2noiuM3qgj3/+6c/ouOhC/uSv/pKwv2Y+ICDAI1D4Aa8p+/bsZctdv0DrmaDNilJ0DdLodJIggUZjLMLqUJoOkSCPiYXLiFJmdTRDSKjskjkKjklE1dDsCm7FRM3l+bdP/zV/sPE8zlq38SWTpVfbFv+95Xn+8da/5TN//7lgIjUgYAbBPvCA1wzHcdi9+QWme4apjkxTdUwyhKgnQhELXRWcHa7DFS46Co1EcXDJaGFs4RAVCs3hCMNuCSFAEQrVYone8SEuDsVY3dz8Gwo9pOm8++xzyW3dxvbt2xco5gEBi5OghR8wL0xMTNC77wD57BSqrpGsr2HXjp0894P7OfzMDtIVB1CIozFCGQ2FadUkKTJYuGQxaCEGQELVCAmNKi41WohdSo6UDOHqKpoNVrnEOV1LKRWLxGLR35BF13TOqa3n53fdxYYNG05xSgQELF4ChR9wUti2zabHn2Jibx9a1aGnp4fnnnyGwYF+xuwy3U6CelPBwiWMTgQVE0EEDU0IqsLBxqWIiUEYDZUqDo2oFDARQmDhMqpUqE3UUlEEGT1EQg/huPaccnXW1LGt7/ApTImAgMVPoPADTorNTz5FeW8/5kSeh+5+gOqRcSZz4whsWmwVgYGBho0kj4WGgkCgoxB2dHRXwVUEBjYTVFAQ5OwqGS1ESdiYjktBmjhCYVldA/HaGrZP9GC4LsnQ3JOypuugqEHxDgiYybzUCCFEBvgGsB7vePHfA/YBPwC6gT7g3VLKqfkIL2BxMDk5yejuQ2hFix9+63tEpqvsyw1TLyNcLJrQEexhkkZiJAlh4aChkCGMjYtjS8bsKkoIKjjE0KjiYDguk65BWbHYY0xTwaUrU8NZq9cxXS2RR9JfLrAklZpTtj1joyx/0xtPYWoEBCx+5mvS9svAA1LK1cAGYA/wl8AvpZQrgF/6zwG/RezdtZuBPT18+Yv/L4nhIqGSTadMcD5NVKXNOGUq2DhIGonQTJwqDuNUSBFiDTVsK05StC3i0mv7qyg0uTEGKyX6KyUMw6UlkmBFWzeqqnCkOImsqWV3tTTn+TojUzl2lou89YYbTnGKBAQsbk66hS+ESAOXATcDSClNwBRC3Ahc4Tv7DvAo8OmTDS9gYRkbG+Pxhx/hiZ8+yJMPP0piyqbVCrNEzfCiNcZy0sRRcVAZoUQUjXHKOEi6SNJGgj6mGaSIgkLM1RnLGzhhSSlsUyPCuFLSUy0xYpVZHa1n1DLIqXD3ge2U0lHe9qH3sWvbC3z/+ee4dvVa6lNJwDuAbe/QIPf1HeSaP/g9WlpaFji1AgIWF/MxpLMEGAe+JYTYAGwBPgE0SSmHfTcjQNNsLwshbgFuAejs7JwHcQJeK3bu3MkdX/wa6miR4V27qM1J6q0IcVdl0C1QwiSKRhGLYSpkCJNAJ4VODpOtjOEiKfnDN+3EWUMNfW6eyUqViUqVGhEhK6ssJ02UEFRVxoTBc3aelRvX87s33cgZGzcg5Yf5+le+wlcefpQWRSOiqGTNKm5DPdf/xZ9x1dVXL3RyBQQsOk768DQhxLnAJuASKeVmIcSXgTzwcSllZoa7KSnlcW+HCA5PW7xMTEzwpb+4lWVOgq3btjHSe4SpaolGJ4TuT8T2kWcdtRQxWU89Ni5hFApYmDgUsChiM0iRRqKEUGglwU6yuEhiaCzBG5dPKBFeFFnalnQR39DFdX96M+effz66rr9ErnK5zAsvvEClUqGlpYV169YFm60CTjtO5eFpA8CAlHKz//xDvPH6USFEi5RyWAjRAozNQ1gBC8TjjzxGSzXEyOQoo30DTFaKdLpxOklQwcJCUiZKHhMXSQSVIpIKDkco0E2aLpIYuERRqSPCEGX6ySORtBBnKWlSSohxWSGrG6Tqall5wQYaNi6no6PjN5Q9QCwW49JLL12AFAkIeP1x0pO2UsoRoF8Isco3ugrYDdwDfNg3+zBw98mGFbBwbH/kaWpjCfr7jmCUqyyRCeLoDFFiCoNJqti49FPExuUAU2gIDlFgOWlS6FhIipjUEcHGpZYQ01hIIIZORo2i6ToF1SYbkzR2trL+orORqQhNTbOOCAYEBLwK5muh8seB24UQIaAX+Ajex+ROIcRHgcPAu+cprIAFoFIokrfBmszjOBZhohQwaCJKxB+3j6D96v+tTCABFUgQRgVyGJSxCaNRwEQCGgp9FNhAPSOyxJhdYSLqcM4557PusvOpKC5dZ64JDkILCJgH5kXhSym3AbONH101H/4HLDzRZILdO3qpFsuAwMIhQwgVhTwmacK0EcdGUsVmJ1M8zhCX0MI4FSwcdBSiaFSwmKCKAOoJM0SR58UE9VqCzqVdrF+7guSSZux0mOjSRs469+yFjj7grQI6cuQIQ0NDALS3tx93oYHruuzcuZM9e/YQjUZZsmQJq1atCs7SD1gwgq2IAa+IlRdt5Bs/uo+0bWFgE0IlSYhJDFaQJo6OhYuFSxKdMxHkMTlCgTqixNAxcMhhkMPAwGYFNehCoU4YaLUJJhQbu86hblUt7RtWsObcjXR3d6Oq6kJHn4MHD3L/nT/EGhmhOR5FInmyWCTW3s7b3vNeOjs7kVIyPj5OLpfjtttu45Enf44akbR21qPrOlZFUp9s4W3Xv4srLr8yuC0r4JQTlLiAV4RZNbFsiwm7jIWDgUOWKs3EUBBU8M61qeL4q3NUmokyjYmLi4aKjSCEgoZCkhBTGDToCZRwiHRHE20djdzyuU+zevXqWSdoF4qenh5+9JWvcnFnKyuvvPBXHyApJbt7DvFfX/4SV77zJnKDw1gTOb579x0Uw2UuumYNV73tQmrra6hWDAaPjLD3xcPc98gPmZrM8o533LQoPmYBpw+Bwg94Cblcjl88+CBP3P8QE4cHCYXC1LU10bPvAKpQGcMb0hmjTAydbpKoKDi4AGh4h51VsYmhUcZmmDKtJFARmLhkCJPDIKlFEPEwoViCM1esQXTX0dTUtKiUveM43Hv7HbxhSTsrl3RSNgwOHDrMkfFphBSkEhFapcM3/v4LvP28y7n32cdQ6lwuOHsdb377JSDAMKpEohGWr+4inoixc8tBth94gXX7zmTt2rWvWBbLsujv72fnzh30HuxBKJBJ17Fh40Y6OjrIZDLBByTguAQKPwDwTr185MGH+P6/fYPyvkEa7DB1tkO+UqKnspMJq8SAM00LMeqJEkEljoaBS44iKcJoCFzkr342LgY2VWxqCRNCpZk4BUxSIoxUBRMRh2gmiYwotK3qpqGhYaGT4iX09vaiTGZZtmElW/Yd4K6nd1IywkRDcWzXQrgG+YkBxib7eYYo/ZVRlq5qZ/X6LgzTQNM1bNvFdWxUXae+uYa6xiROpcwzm59gzZo1L9k34LruS46MsCyLarXKk08+wXPPPcXoaC/JpEoqESGXLbAvX+She24nGqrjiiuvYcPFF7B67ZpA8QfMSqDwA3Bdl0d//hAP/MfthA9O0WmnyRXy1BGmrhylYEKL1Ojw19l3kqCEjYFDCp00IUYo+2txvOVZLpI4Os3EGaJICYcMYVwkQ5RwVYVyWCEejZJpqkM2p7nomisX3aapvp6DtKQS3P7Qo9z19H6a6pYSCYWpmg5ISbFiM2UoVJUYd257imhbHfk9Y+jp/WysGrQva8AVCo4BKCaKgFgqRKkywDO7H2Xd6vWkM7WMDg0zNTCEbRhMF4uYqkIuO8n0xASHRw+RqtVpbEpw/nmdLFvWgnShUCgzPDBB7/4RoqEQm594mFjVITs6xqVvvGLOs4Z+G3Fdl1wuh+M4RKNREonEQou0KAkUfgCDg4M88+MHGN91kPSkw/bKFFFb5aBTpYpNmhCq33q3cNhGlnrCVHA4SJ5WYmQIM0aFJBpFbLIYRFARCNpIEkFjkBJDlClhkdGSOHENs05l1UVn8vaPvI+Ojo6FTorfIDs5yX2bHmMo73Lmio3UpNNUqgZT0xVwYX17HQc1g905iZJqI7GkjUxDDZNEueuBwzQkD/DWd5xNKh3Ddi1AEA4puHaVob79fP4zf0Fp3GRtfTv1qST1iQRhU5AdGGTQLKA2xonGDNav6+bCC9dQW5diKlegtiZJKNREa1sdsWSEbc/1EE0JxiZHiPdGOdC+n1WrVy908s0bUsqXNAaklAwNDbF/+052vrCVnp17KIyMU5ycIpfP40R0lq9fwzXvuJGLL7+M1tbWBZR+8RAo/NMc13W5+/Y7mdh+ECNfYaBYotWJYEqbFtI0EGOKKhNUSBKikSgHmKaKQxSVw+QxcEgTwkXSSwEFMHCQmkJesWhWEow5FUYoUQkJmhpbKdWGufQtV3P9O25k9ZrFuc7+yJEj3H/vnRSMMtFImnBYIR4WNKeSLG1KMJQtsauvn1IoQm3XBjJCw77lBBcAACAASURBVNJMYvE4jW0tdK9YQt+uXdx/14u87V0bSafjWK5LpVSld984jqyhJaqxam0jlaqKLLoMj4wRtgzWdrRzRrSdXxzeSXeqjsm+SWrekiCVjhGNhxkeylJTkyAU1lmxso0DBwYxCyb7+/dzzor1HNi2gxUrV75uW/lSSp5//nl+dOftHBnYj3RdotEUV1/7O9xwww3s2vYiEzsPkB0cZnTzVqID4yjTeVpdhYyaxrQkux7bxo939rLr4ie49iPv5cJLLll0PchTTaDwT3MO7N/PwAu7KeamKeYLtNtRVCT1ROgiSQ6TDGGaiTFEiRQhYujsZ4pOkhyhSBEbC4mJRQ4TB4mqhZiKmDTFMhCNUZtupKO7ie4Na2lc1c21N7510Xa7pZQ88tBD/MfXvk4iHsPILENTU4wZDlZ2lFXNDaRTCXQFnu9zsaN11NW0YtkFxktldEWQyxZIJBroWrOGA89m6T0wxtnnL0GR8Myj+yhXk5y5NME6LUI6EuOF7SOsauykHE1waGiQbDVPjQjRrIeRQrC2pomePQO84ZqNuI5LJZPANC2isTCWZdHR2cj2bQfJl0wMy0RWTYrFIqnj3BmwWHFdly9+8R85sOcJLrtkFTe/9wbC4RD7evp57PEf8/3vfYMLujYw3HeEvhd20mgohCU0a1EyagjHlTSEIlyf7OTh7ABjz2zjERRq6+t/q3o9J0Kg8E9jXNdl21PPUcxOY5sWaakhcbAQtBKnjIWNS5oQIEigU/ZX39QQIUMIQYpxSthI+ikxQIm6piYue/db6WprpzRdQNoujR2tLF+1glVnrKOpqWlRtzwff/RRHvjpg9Q1dbG8I4F7YATHjdHZ1IFjG+waOciZKli2TSSToRRKo2kquqrTJDVy2QJVw0LXVZpaa0k3t3NwXx9r1rWyZ1c/u7YOc0b3KuKyQkMqgWlK0rpOqVwiFQ7TUFfLaHECXURoSifpmS7Q1lDDgYMTTJ9XoiYTp7k5w8HeEWrrwkyMT5NMRUgkIuzdPUC5UkUNaziOs9BJeUJ85zvfYujwZv7qk2+nva0Zy7SwDIMLz1rJ+pVt3P6DR7jzOz+keVKhS4mzSk9RkTbNWgwhvKHHnFGhapksVRP0TOTI7zvEpl8++rru9cwHgcI/jSkWi/TvPcCSTBO71BFKjosNxAnh+FcSxv1LCR3/0LNRKgggQwgTSRsxDCxShBijSl1rE1/41le56tprFjp6J0S1WuXnP3uQ9evOY8ueLZjVAhFd4LhgWCbpWBzRvJSe0V5a03EUPUwoFMF1XYRboT5iMDU0zJAp6d2rEAqrhFWJWulndKCfgzsP01C3jIvWtXLgxf3oqkrJNImHdIyqCVqYWDhC2YqQrxZpq6vHGs2h6Rp1MobrePsdLMtF11WkBNO2GRvPoeoKqUyc0VyWWFMD8Xh8gVPz1VOtVnnkl/fwsd+7hPa2ZsrFIpoEXBeramBXq7zz2nPYveMIB366l7fUtqKjEkElqYZwpUtYqMSFxoRTJY6KZjoM7unhyO3/TefaVbS1tVFbW0t9ff1CR/eUc/p+6gIwDIPp4QnWdSzF1RRs4W2oKmDhINFRSBMi7B+JEPaVfxUHB4lEkkBHInARaEJj4/nncOXVr98TNV588UV0NUpdTQ3FUomwBrUJiIdVcoUpXNchHk1QFSGqpoFtGoCkUJ5meKiXw3mo6VhFU/sq9NQSrFAHg6Mme3dm2fnMGHbBZVlTElVVUHUV23FQFHClRCje+LIiBK4ETdeIxENIIF8oUTFMhKIQDnv7FBzHxbYsBgfGKZcNKhWDxvo0g5PDtK5a9ro8wuH555+nJinYcOZyyqUSulBwXRe7amBWKqgupMIRLj5/JUaNjkBh2ClTcCymbQNbSmwkEaERV3Sk65BQNJIOjO/Yx+f/+M/p++XjPPfT+/jFPT9laur0unU1UPinMYVCgZ7+Q9yz+TEGylMUHBMLh35K5DGo4JDHooRNGRsDlzgqApjEIISKjUsJC0NxqYkk6Ozqfl1PjI0ODZFK1xEJhymXHbL5Mh11UaQsEdVMxqZGyZdLOEqYwYkphgb7GBg+RN/hF6lpbqJz6XJAR1OidDe0Up/I0FTfSufq8+jsPpPYkovZcqTC09v2k6hPM1EuE43oTJUqxKNRpCIICYVKtUJtTYKByRwrVrezc8chBnNTqKqKKyXlcpWQrrH1xYMc7B3iYM8QDQ21ZBI1lBSb5Wten2PVQ0OD1NbG0DUN4UoUIbBME2nZuI53HhNAQ30KJal7F94rIerUMBFFZco1OGIVcXGJKRp5aRMWKqpQ0B2J7Bvln7/676xubKXT1Xjy3vvJ5XILHOtTR6DwT0NM0+QHt9/Bp95/C8Vtfbi7BmmeFsRRyWOh4DKFiYNLCZscBuNUGKWEA9QQJksVG8kARUChNZYhlIoSSyVe1wpf03Vcx0UgqEvU0tM/RVTXWNKgoVAGShwZPcTuwSEO501cR2CVxoknUrjRekazRQwL6tO1VKwqlmsRjdfQ1rEcG5XVbR1kOtYyZCaZLFqM2FUGJqYouS7RSJRwNELFMigXcyAURt0qzXUZ7BD0jI7w8C9f4N57N3Hf/c/yk588yZNPbOfAvgEiUQ1pC8KpFCs3nkEmk3nZuC5GUqk0hWIVwzDQFAXLssGV3k/yq+XBuUIZ3QYUaFSjICCjhOjWEiQVnX67hHAlo3YFXQg0BGXpskZJUj40xK1f/CcAlsVTbNv07MJG+hQSjOGfBhiGwdjYGNlsln279nDPbT9g8NndrLISdChNJNUwVdvkISZpI04KnQImht+6TxLCxWGICnE0JIIoKjqCqiLo0jOUhYOTitCxYulCR/ekWL5yJY89vAmAFd3d7Lx/B5tiw6xbUss5K5Js6c0itRjJsEVc2KxrCTORH2NUZjBsDdOwiYZU8sVppvPTRHWdcFhHU1SUaJqQa6I6krLU2ZetMt03SHl4mvZwglLBoqW2lsnsCBY2j/T0smZdJ+P5PFajxsqGDjY/t5t8vkJrWw2JRITeQ4M0NNSgh6K0di+jtq6dVLz1dbvT9oILLuBb3/gnDh4cZFV3K67reh/gGRfzmbbDlq29tOdDZKVJUlRIq2HyrkVK0alRw5iWww5zkpJr0aJEGbBz1IgQtoAmwvTt2Mt99z/AO37nRopDA0xPT5NOpxcu4qeIQOH/FmNZFluefZ7nHnmcnhd2MXDgEJPZLLJkstxJsi7UQNWoMlgZJy8N4uhkqXKYPIcpkkCnkSjjGDg4JAhRwcHExcRmJ5O0izSaEPRHTNLrV3PWhectdLRPipUrV5LMxOjp66O5pYnGZD29vWOMTBSor4kybYaQwsSYnqSpJklVSjKxOGrUYWr4AIapMCVtIiGNRKaJukQUFYWK7RKKRpjKl3CFztDYFCJfJD5psjTSQd4ss6M4zHN9e7BlhaUrltJcn+TFsSGiSpQVbZ0kUmGuedNGamoTmKbNtq296HoIoai0tXcRi6aRjsaSJSvmJS2mp6fJZrP09/eTHxmjUiigahrda1ezYvXq12TJZ01NDd3LzuJHdz/DH//+WwmF/IPqkN5ZTNLh8c172bH1CJfJGEvDabYZWTJqiITQCSsaLi4jVoVeM09c0eixC5Qci3Mi9ZSkzbhdJmZFyO4/zI59e6mprwsUfsDrG9u2ue+ue3jg2/+NGJ4mPmmxvKowUtQZKBeIEENIm8lqkayskKVMFgvHP9zsKtpREAxRIoRCkggTGP7tVCouLtMYCFFiLCoJL2vhwmsup7u7e6GjftJ86OYP8JV//Q9aql3Ud7Rg7y+gWDYTo0WmZYyENcLqpjSjeRPDcmlNhjAV6E4mqJQc8qaGUGzq4wksS1JxbQzDZXo8R8S1sYRLrCRoKAomcpMYZp4YkDYtpoRForaesbEJVm3soi5aQ7IhRDIVJZGIIhSFctmkUqmyf88AmlAwpyx6X+hh+5N7UdU0+TGDlevXsGTpUibGxxnu66dSLqOEQyxZsYyurq7fOKCuWq2Sy+WYmppi9+7dPPvok4wcOMz0kREqlSJV0yQWidLS1EgslaBh1RJ+949+n9Wvwbr2z3zms3z0I+/jC1/+Ide/6VxWdjWiSxgameLhTbu47+cvsmxQEFJUTFxW6xls4TJqVZg0i0i/OzBol6lTIzSqYdr1BHVahLBrIREYpkmtFmbHtu1c8sbL5z0Oi5WTvsR8PgkuMZ8/nn7qKf75E59hyZRKkxliKDdBxTLJWkWEC8tJ0UeBvH8Ewh6mWEmGDGGWkPLPt3cwsHmecSTQSRIXiYHLECVUBKWowqpzzuTiG6/lzR94F83NzQsd9XlhcHCQn9x9L9u27mLvrl3U6wrFwhTIMB11dSghjfq4d3hEW22YZ/uncDPdlMsuU5aN7ZpEazsRrnccNLZNaXAfdYkGCobFVG6YNVUdRvq4qBxFAaQQZJ0KLzBBSbcRy1O86awzOTgywnA2Cxq4UZVoS5yqbdO3Z4h6NUFKCWMVbbSqQnO6nubmZkoxKCg2S1o6KIxOMLi3F8u0yIUc6td287t/eDOXX3kllmWx/YWt7HzmefY+9yJ92/dijE6huwqqBM2BRqKYwlu9VcCiJpFERnUmMwq/978/yeWXX04mk5nX9e35fJ5PfPx/MXB4N8m4ipBQzlfI9uVwJyu8I9qJEN6KMVdKIopGFAUTl5Jrk3dNeq08a0M1uEgkglolREHa9JtFjtglbrriagYUgwtuvI6r33PT63KT2lFe6SXmgcL/LWRycpL/8e4PEdo6xFmynn2FUQzXopkYVddmmirTmExRRcM78riTBO3+EcYKChF/Bc4hCnSRRAFaiOECVWxsYK+SY6RG8L6/+Tjv/uD7qa2tXeCYzz9jY2P85799iZEdz6BWipSsJs6oaWagXPQOhJawtDFOrlhirx0ha8YpVSqENQXSrdSG0ghXMjl+iKTloIeT5MoFolOTOGaZ1QNZ1soELuC6DhLY4U4yISvsCk/TmszQocapEWFvdY5rMWDk6S/nqbejtIgImlAQCKqKy7isMq1ZxPUItSKMbdq0aAnaohlUFEzXYa+cZKpO59L3Xs+q5SuY3tfP1l8+TWKqijZtoEuNWkdjtDDFtGt45URoJIWG6ipMKlV0RaeKxXDUYu1VF3PWpRdy4dWXsWr16nmbP7Btm4cf/AWP3P0z9jy3leR4gbNIsy0/ioKkRgnj4lJ1HfKOSVhRSaghUorOtG0w7Zp0hpI0KBF67QJFx2JFOM2gXWKvOc2bL3kDvXaJd33qf/HG666bF5kXileq8IMhnd8ypJT86L++z9iWfVwrOpmQZZJCZ6WSBukdWfwCoyQJs5Q0w5SIo9NKnDwmEXQSCGwkY1ToIM4yUoxQZoASDUQpCYeUHmVjtI2tyjSqUH8rlT1AY2MjN73/w9z/vTLLU/B/f/wsj44oFC2NuBrGcC16p8c5rzWNOT5MtqqRVDQsVcN1dcy4xMiP0SRV1EiakfI0bn6SDifCwfwAcdsB1VsuJ/1fvQwzRglhOCyxIjQoOroKmqITNV1UJ0YNCmMYxKVGOwks6ZJ1q5iYTDgGK400FpIQOikLhFlFKgr1kTiXaW1smhzmga99j96uJbgVg65imHK5jFOwUF1B0RXYjkUClT4MaiWoQqWKSY2tM6UYNOlx0pUQ9vZ+hojw6NAEY1ddxBuuunJelL6maVz7ljdz9vnnsfmpp/jJd+/gvqefJy1cFFsiXKhgI6UkpuiEhELeMZm0K2Qdk1WhFBGhctDOs9/I06HFqbg2IRQURWWqUEC2JDn34otPWtbXC/Om8IUQKvA8MCilvF4IsQT4PlAHbAE+KKU05yu8gNkZGxtj04/vJyZVaqJxDk1PcpZSh+HaVF2TEibTWHSIBKWQRYceIy108pbBiFVhhZOhnhgOLj1Y/h20DrX+7VUJdCwpMW0L6QhaiXJg156FjvZrysqVq9i17mKe/sUPmCqY1NfXsrKmkYSi4boOg6UivzgyStwStJVzOMVxxg2JrkQYjMVpbVyOjKQZyw0iSmXSpstkYZIVUxUsNBzXOwLhaF/bEZKsrJIhTFmzmHYFSSeEkBDWVFrVOMKUuEAZm1FZJopGI1HCqExiIoA6wjQQZcI1sLBIyTDjxRw6Cro0sGWewdx+GsMJspaKZoOUCrVqlKJjUpE2AkEXCXQUamWESaqMU6bkOuiGwFQkxvAYbiJEU6qOvme209jWwtp16+Yt/evr63nrjTfy1htvZN++fXzgHe9ifH8fHWqUVj1GVNFxcSk4NnFVJ6PorA3VIhUYs8vEhE5a0bGlS0goZB2DjB7hsFPmg7f88et6KOfVMp/r8D8BzKz5/wf4kpRyOTAFfHQewwqYg82bN5MpSEKhEEPGNHUyQkTVQUqG3SJPMEyjGqExGeHsaC1RVUMKSTqqc36mjmrS5ICao4xFG3GWkOIwBWz/EvIqDkIIEoQIW6ALFSNfXOhov6YIIbjoDZexr19hRfNawnaBbGGMsm3gSEFtKEaYBP25Iu7kKB2OQm3Z5LpqlCsnDOI9O7EObqN5fJTK+BHMwR7axiZplzEs/6YwAAHY0mGvzCEQrCZDBzE64ymcMIyrVcIhlWQiTGsmTVhVyVJlSC9TrHPJNTiYCUnMP5ZaQbCXHBNU2OVOMuAUUSRoLgy5JTpljJSlkChLkrb3wQAX23aYlFXqieIiaSFOzD9HaQqDBmK0EaeRGG1ujOaiQnHvADt37CA7PMr+LTtwXXfO9DwZVqxYwbqWTt6y/lyqCoy5BiNOmYjQWRJKkVbDJNQQmuqptjWhGkzpEFM0MmqYIbsCukY5Fea8t13HGy677DWRc7EyLy18IUQ78Fbg88CfCW/nzRuB9/lOvgPcCvz7fIQXMDd9Pb3U60lKNfUcGZ5kqUhi43LAnqSMxRQVLow1ElUVBt0iVdVhVSzJEiWJJSUtsSjZhMFU1aS/WCRjh6iRYQ5RoIYQBUx0qaJrKhLp/dVff1v4Xy1PP/kUZ6+/mJClkx8a4shwL32D+3AccC2XqUoOxTGw4w1oyUbM8BR9hqDWVrhKRrErLoVykfz0FA3S2yiUx/ImdH1cBPuk1wJvJEq9GiUuQqS1MI16jKFynmGnRLOIUxuNkHDCZMnxO+uX0RZJkhQ6k3aV0pTgUG+OFSWFLpLefgkcKtJhRJYYoYSO8O8ZdnFcFxuHnHAoSAsFSPgX24xTQUdBIsljsIw0OgrjVAjhzR2Y0kWvWliHxthqFIm3NDA9PU1NTc2854NhGCiWyzvPuJCBsRGiFZu0VNCEQApYpadxAUs6OLgMOCVKrkVc0SlIi6oimQ4rLLv8Aj74P24hFovNu4yLmfka0vkX4FNA0n+uA3JSStt/HgDaZntRCHELcAtAZ2fnPIlz+hIOhRnH4tyuVfx45GEiliTrFhigyDQGSV0nqepk/3/23jvKsuuu8/3sk8+5OVTOVV3d1UndrZZaqWXJQQ6ysccRk2x4Jszw3loMj4FHGg8wpGGAhx/hEcYMNgNjjB9g2ZZtZElWakndanXOoXK8dXM4+Zz3RzVaeDDYoDYtteqzVq2qdde995y9d9W39v3t3+/7ix08KeKWZI6kUFiJbIa0BAOKhR0F+FqMIcuoDrRcjzW/Qxc6TQL6hUlITCCDbypYxVdnVec/h2OHTzHVv4eEkWAG6OvuYWFmAadaJ2rUOZ+QMTO9xE6NQT3JUhiTshJ0Qo8vl68yIHskNYVuQ2Wu0SDV0fHjmK0iiysiRBxzKW4wEzfpwsAjRgjI6TphEIMKBdXC9iKqgYseKKBJdOdS9CWSaMgoyKQ0nXxPgq6ERXTSob9jUcMDBDHQwqeOR4eAmA4dfIZIIdgoZgWYp8U2NrJbFMSGdQbBtfeIAIkeLGK41pReUIps7FqLqmhx4sUXeaf9nd8Swdc0DWFuhLdum5xibXqOSqNJHkEsCY65FRQ2wmMKgpAYP44pRzZtOUYtZtj/zrfyH/7Tz76mQjl/x8sO6Qgh3gGsxXF89F/y+jiO/zCO49viOL7tldbP9NXIth1TlIRD0UpT6OnmYljlfFhhhTYeAQlZIRAxThRSNAwCKaQaeSQ0hZSq0okCmlFAJXIZ15M0ZZ9+y8SXw2s7UoEvRXiaQErqlM2QLduuT6HPKxnX89A1A03TGNk2SZRMkB/ux+jNc9ytoGZ6EapKh5iS79Cnm8yHbXxTYnhslN5Ckn1dWe4f6uUNW7qRewOuKlVmaPA0SzzMPMdYZ4gkuqwQyjG+FJPQtGuhnghZlsihUYkcQmIqksvUeDctfGTAJ6JEBw2JLckcRr9BE4+AmHNUuEoDn4gCOt61EN3t9JDHeKnCOoVK+lqT+RYBaTQuUmOB1rWivBazNKnjUsdhnibzNGnjo0QQVdosnL/ME488RhAE32BW//nIsszu++/h1Mosr9+yC7WYJZvNUIp9ypFLt2aSU3VUBAtBhxfdCsdoUB8psP9D7+GnPv67fPS//PJrUuzh+uzw7wHeKYR4EDCANPAxICuEUK7t8geBxetwrU2+AbcduJ2/GirwzOJlkqEERowsx2wXGeRI0IwDEqFCiEafauDEEZ4IUCPB4UaJOAQ5lqgHPkLEzEcdCpJOQdfpdAJysoWZtBApg+PBGj1bdzE6Nnajh/0tJ5tLU2vUsAwTXdMZm9yC7djYSkSqOkhGS1Br2biBh68rFHSdpbCNkojIyhazDUE2dJBkBSktMagnaCouV1cbjJFmWCSZjZs0Ih9Nl1EjCVeOsKMAITZ2qsSgSBJBGDMXNqkkXd420svcbI1MpFPDxiGkCxMVmVSPycXZFeq+RwIVC4UyNqs4+ESMkyaFRgOPSzQYwCQgRkWgIjF3rZtZE48BkuTQ6CdJTMw0DZbpMEkWBYlh0hBBG4/lUpXLjx7muaEh7nn96667t9K7vv39/MpXnmK00eDde+/imavnUUoms+U1zrRXCKOQRuQxsGOK933n+3nwHW+nWCySyWRetZYT14uXLfhxHP8U8FMAQoj7gf8Qx/F3CSH+EngfG5k6HwY++3Kvtck3JplM8tYPvZ/f/rGPsi1UGdR0skJnmCQ5WWdZ2qhEXPA83NggJsaJAoQjmJSz5FWDVuTTJqQVeZQDnyW5TaCETNNki6lTURv4UpvUlgH233mAsambf4d/z/138ZW/eob+7r6XHjN0g+WVebJaAimUkMKQ2Le5EPgkJZV0QiapSawEHdY1mQtU2JrNo5sab9naT/bcIn97fIk1z2PNdwiFoMvfCMsoksCMVa44NcxIJS8MYj+mE/oshS0cIt5w+1bKik8l2vA40pGp49KNRQGDNanNjNqi29dxCNGQMJHpx6IHiyoeFVy6MAiJuEQDHfmlFNGLbLhITpKhjodPRBcbB80ZdCQEC7TYTREdGZ+QAgZxw+PY088yunMrkzun6Onpua5rMTY2xvd89Mf55M//V7aVLfb1jbK7b4TZ8irnS8vMKh6/8Iv/kTe96U2v6WYnX49vZR7+/wV8Sgjxi8Ax4OPfwmtt8vcY2zLBtqEBlIvLmEJBiwWX4zqOFOJFIQlZoRQ79IYGaaHScSL2GTlMoeDHG4UsaVQ0ISgInVroIYSErwj0Ypq+Yjdy0mT0rj2YQ0UGBr7u8cxNxR133MFjX/4q52cuMTW68Q+u0WzgNGq0OwpRELLcWCHrSyiSzErUZiCpsx7EaIog0kL6RtKMjOTp78tSa3e4e/8Iqm7Q29eD3fCYLdeoT7cpuCaJSKXi2BAKKr5Lxbfx/ZC1yGZV7zA60cXwcJ5ziyWKmDTwCfGwrvUsAPCjiESgEBDTj0VETERMEkEanT5MzlIlgcpOUniErOMwS5MyNTRktpKlm41K4PPUWKZDQISKRD8JQmJCIiIEPhGhFDMpMlxca3H8yUOM3777ugl+HMdcuXKFY88+T3V5lZ333sH88hLHrpxFiyW0pMmt3/8+fuY9775pKr6vN9dV8OM4/irw1Ws/XwUOXM/33+Sb4+qpM4zku7gUzxMSUVZdtpgpxswkpqpSDRxONCvMeW36hUU+3mggHsQhnShEjWRioBZ6KEgkhMpKYNOX72Kqf4ymEZHeMYox1c/r3vbAa+Jjsmma/B8/+sP87m/9Pk+dWGGoa4RmvUG1XGKu6SMJhZ2xwQ6zB1WSKUU2CSWmItvkTRmvElBIJZEkQa1tgyoxOVTgxHQZiBnty250a4oUeiOL2mqHjGqwIloolsB2QwIlgiTcPzHAXK3DiYVl3EZIG5dudBRkOvg4BHiEnFtfp+357KCLNiEJ1GuNbeRrrqewlQxLdDCupXL2YKEhs0CbNh4GMiExbQI2DKJ9UqjkMUmhUcCkgkMRgzlaZNDJqhZ9xMyeucjVC5eui1eN4zj82cf/O+svnGY8kWcimcSNJPoyffDWKXr37OCe19+PomzWkv5TbM7OTYbv+3SqVex2C8e3MTSJXck0E6kMyCBLgl4SqLrEF1bmmPfa7BY6jdAjAtRI2ggNRB4lzyUrDBQ54DlqTO7awepklq27drD//ruZ2LIFwzBu9JD/1eju7uZnfu4nOX78OIeeep7TZ4+ysHKeRsPndT23slXPoQoZYoijmFYQ0GXqdOw2ReFwedFj1fcJVI2xwRxqtY2RM7ncqLNSaXFg2yCJdI3pS1UiI2CuUmGgL4WhyQSxTDovMbltAElROHKxxPGZZfpzBbLdRRwvxHRBeCFeHHKpU2NtqUkeDRUZ89oOPIlC/VqvYo+IHDptQqq4pND4u2PWXkxmCajiUcdDR6IHkxoeZVyUa9k5ARFrdKji0YMJQmBpGkUipjtVZqenX/a8+77Pn/zuHyDOTHOwf4KZpUWOXrmM7zhomoaaSuC12iRSKW67846Xfb2bmU3Bv8kQQhCFEUHgo0uCjKzSkzTRVRlT2uhW5RNjKSqTZobDWnM6dwAAIABJREFUnTKzQYcwEAyoCTwiSpGLG8KAkqESOVT0gLe8+338p9/+r8iy/JrY0f9jaJrGgQMHOHDgAH/8B3/E/JOPMxwpOM01QjWDUGTcKERDYanTIVZC1PYybSXA17po6Dk6YUTLMXj6dI3qepWpoRTGcILT7SqxFqKPa5ybbSBkiUxBQhYRsS6jiQDNC9DcgP05jcjRObyyyoGeLt538K1cWJ7lhZlL1Gyf45fmSHqAqvBCWMKPIiIiUqhk0bFQCImREBjIOISk2PBJSqLSwKOJz24KrONQQCeJdq29ZURITB2XdRwSaIyRokOITUhCNVgLXFzfQzHUbzSl35CTx44z/ewLGKU6z37lEYTvkxIKCVVDEoJQlrh6/jznFubYccvu11xu/T+HTcG/yVAUhUxPN/VGAyQoWgbdxkbJvZBAvtb9Rw0liqbJkJVAakqUWz5VrY6pqBQ0iy5doxl4rCkRfjLBngP7UVX1Vd3N6nqT1HRsJ2SX3oXT6VANL1MykwSqiRxDs71Gs77OLXkTd2KMbCLJbBARpNJUFZWFWCbq6+GhK4vsatrsG05w51SRjhfiRS7bMgZb0waWLpNWVS5Mr7E7YwJQS6jEQvDc1Qpnj19kesFGzaa4/YEHuPe+N3Pq1Cl+/t/+GGrDZkucohsLREw1drlIlTYBPVjo10I2go2D2oAYn4BVOtdOAzaS89Mbv0EMkOQcFQwUMuiE2AyTwCXElyNSkk4n8KjHLo4GYxMvryFOEAT85q/8GutPHyEbgSpJ9KgmfbpJXtXJqBqBEJR8l6NPPMcf/M7v8qM/8eMvc2VvXjYF/yZk6749/M/IwxSQUjZCDF4cEgQbOzMhYNWx6TMslq0O865Ll5tgWC3SEj5ClmkR0RQSqUwep09l6rY9m2L/v2BYFrKiEXuQkw1ygUKn3qQd1kgImR1C4ZlWzKF8zISi0lZNRkf6efHyHMtRkoGdU+SzSayhGnZ5kbU44AtHl7ijV0UOYvoSKl1JDUWS0aQN6ZWAMN74SmsSugJbC0lqQ7189Nc/RrFYBOCZx5/kjuI4/SImbQvwfCQE3ZFFIdY5QZl1bGJiVrEZJMkibRZooiGRRCOFxgwtUqjoyETEpFAZIEEJmyYeHhvtIHVZQRYRoYC5sMFFqmQnetlz+zc0cPxH8X2fn/nxn2D6q0+xTUmS0lS6NINdyRyGJGNHAY3AJ6moDOsWHS/isf/5l3zX936Y7u7u67PINxmbOUs3Ga1Wi3K5zIurC5Q9G1naiLU2Ao9OGOKFEau2TVJWyKk6WUVjMW6xImyEJDbsZWUDVVbRUiZVKyKxpY/JrVtv9NBecQxuGcNKJ+koEiIWOFGAHYYUhU5eNkAIlFjFT+eYtkPkQjfTy2W0dD89A8MkZZ1Wy0WSJaRkjvU29PWPcWK+RaVh43shiiSjSoKm66PEsNh0Ob7Y5PRCi/PLNp4Doa5RtPSXionK5TJXnznGuw6+EbM7R5jSCKQNB1RPirAkjZ3kWaYDbHSTiolwCchcC92ExBQxiIE5WqzQwSeihU9MzBwtzlFFIChjU4tdVqI2J6ISZ+UqQdbg/rc9wOj4v2yH7zgOv/VLv8L5T/01fZJKIGJ0SaJLM7DDgHXPwYsiCqqOG4b4YYgSw0AEn3/ooeu1xDcdmzv8mwTbtjl66HnOHj7Gl/78M6SrHmv4PLy6yFSqRY9uoCJTC10AeoWJyob/SBOf48EabTdmwMxApNBUQuy0xsTenbz+e9+PaZo3eISvPEZGRxndOsnlw2fYnpmg0qgRBCEdKWReNFHMGEOTUSXwkylswHEjeiYGaRBQadqYlkZG1XB1HckxCJFY8QxU22Om3GZbVxJFklmsdqjUHeLYoFsxUUTAmuMzIaXwFxyW4nk6nQ6apvHCkSP0xyaDg4OoBxVOHD6KHXqIposbbWTeKJKMFStclVrsFgWcyEeLNsI7F6gxgMUibUZIbWRp0WGNDhHgipCROIUXR5SETVm4NGMPR47JJ1JY+RRb77+Vd33ou/9Fh/pBEPDlv/ksRz//JdKxhC9thHJ6NZO9qcLGc8KQcuBSD3wMWcIOA0p+h13FPs489zx8//df38W+SdgU/JsAx3F49HNfZPn4Rb7w0EPYy2vs0NMsBDUSskxOUenTTHRZYVJOIyO42KlzqLpGKfQomiYlEeFu66GkmyStBDunptg1OUVF8dm7f9+NHuIrknw+z33veAufW1rlqYUF9lvdXHYrZC2XW/NZiorOIbfKgogZzufB8ViPJNQowo5jUsjYDQ9FhUq1TdL2uNzqkDXTqKbJ5dUmw4UEXYbK5bkyA4FBt2VQtj3W2i7nlprcWcgz2wpwbJ2TR4/xuje+nvp6laRiIhD09vWRf/DNzM7McOaFE7TLNTp+BCHEvsycaKHICrqk0lEjGr5D4AlOh3W60CkIk6JiMRPU6ZEsvChEFypFM4miKBh2lU7gUpFclISBvq2XO77tLXzwf/sw/f39/6J5nZ2dZf38ZezVNTJCMJFI0atYLDptACQhUBSZbslE8h1qvseM2yYhy5iyjOdsurD/Y2wK/k3AV778tzz+6b9k5fIZugOPTtajErURYcS61+GRaoduJ8GAYdIlDCxJxotiTjTLpEyLIS1Dd8bktjvu4W33vRHP91mprlPTQg4++JbXrO/IN0IIwZ1vvA+96fH4Vx7lr48cxzICtuaKNGSf6aBBKbZRWwpyBD2pBKU2REGErshYio4cB3ihj9/o0Kg1KVg+3V0mkapxvtnhz568zEBCISd0upIGx1brrLZdSlWbMWGSN3VeWG0xtnuc0qU52ne2MVMJ6qH/0n1qqsbk5FbGxsdZmJ9naXYev91hdi1kfGKc/GAvfQODZBNp2rUGZ8+epba0Rr1S5YnaOt2StZHq6ZUomhY9Soq10GE9sCmZPm63wa573sZb3/1O7rrrLl6uJ9b06bPYjRbCDzdsj40UW8wkpzpVXmyuM2wkyao6XPOEuuI2aYUek2aWkuvSNzrycpf2pmVT8F/lXLhwgS/80e+zx9QIiCimFQpmBkuXcPyIkoixEgallkfZ9pjz6zh2QMqTkZGo2h5vmNjNmahFTQs43VxGNw1GDu7mrvHxzRS3b8CWyUlW9i/ynmyWT2oBzsp5SkqAi6BPSbNH9HLIaXFxZpHMrhT4Nk7kkcIiIAIB1XqDrjCi7Th063DqwjzKyjxXSy2yisx5OUQJTFaSNglZwQwlxtQEE8UER9fqVDzBbT19ZGKVlZUVbtm7hyOfephbo+hrrAUUWWF0dIyh4WFK1QqzZYuf/Niv/oNK2DAMmZ+f58yRYxw/fISly7P0p/JsGZug2qpz5cpV1to1BqbG+c63vJEDBw5c10PSRqVCSIQkCWQhSMkKqiQzZaWQJMFq6HDZbUIYockyPbqBHkg4xMzh833v+rbrdi83G5uC/yomjmM+88d/xBv6unjy1Gl0JWAgY5IxJdY6Hk5S4fa+LIfmy0QZmV1be8imdBw/4MpinbnpOmsLNr4qMK00b//297L/tn95VsVrEUmSuOf193Gu6wz2Zz/BPbdPMpRN075UATtgtW4zKuDc3DznU0kCAW69gUjLtNyYdtsm4fk4rktaDvAjFcoVtskWsh5jWgrrtkM20Cm6GglNoWjqGIrM0bU6VxshmUyOvpFBBBJBEDAxMUHXLROcOHeJfaPbvu59n1mf5+C73/p1bQ9kWWZ0dJTR0VHe/v530+l0mJmeprK8xqAkeMPABxkZHUXX9W/ZnA709uDFMbIkYUchbhwxlcxxtL7O7ZkiBSuDAOw4YsFpMee0WVQhte9W9u3bDEH+Y2wK/quYCxcuYDTr2G6A4zZ412QPPZaChuCQaPDO3YP8+al55KLBm7b3k07rdGUsEqpK0/E5emmVLz0/w5cXznHLvfexa/fuGz2kVyWyLLNrzy1s27UbI5hlcs8YyymT1nwdu9yhVW2yvRzw4sljtHp6qS6XMfsmyFlJujJpUGVCu8ZQsUBpdRlVcnnec+jIEelOhOJr2GGMIlTCSGKx5bNqt1C0BLeP9HDSdhkZGeHiyvxLh+vf8+9+gP/3l3+DJy+eYEfvMMV0jjiOWayscbo0R+aObbzzve/5psZnWdZGy8Lr2Lbwn6JnbIQIhdAycJ2Iiu+SkFX6dJMdiQzHmmV6VJMuzcCLIi60G5zs1Bm9617+z5//6Ka9wj/B5sy8irly8SKTxRxPnDvNLV1JBlM6YRhS7fh05ROstl0WA59v2zVMb1eKgXyCMAYJgabJHNzZj6wI/vjhcwzfsuNbtmN7rXDXwfs5/Je/h+eFTO4cRt+rsjhbwm47TMUxd4uQI2fm+OujVyH0cNNZZGsLe7duQQwWePRLf0PCKyEyJvWUhW17SHFAnwutFRcRWwQhRLLEruFB+pMZnluZY/fBNxJGER2dlw5Ks9ksP/JzP8UTX/0qhx5+DPfKRaIoIjvcy8EPfgd33333K7ZiemJqihcuT3PH6+7l5N8+SiPymXaaNEOfPs1gZzLH1XaD0+0aK67NrG9z94e/g4/+ws9vnjd9AzYF/1VMFIas1+poUUCXqeEFEeWOy2zTJpNK8fkry4wOZOjJJ+jNWRt9PsONfGYnjECCqcE8e6d6KK3M0m63SSQSN3pYr1re8MY38qVP/Qnr5RYiijA0hb6hAoqiYDsuq6sVujIp3nHwDn7wA9/OHz/8GEYqgVxZ4rHHvoiV1lDHpsBK0qtqRJ6H32lSWltDiHUu1lvcVRwka1mEMVxorkN/P/sPHODc8gzbX3/ga3a3lmXxtgcf5C1vfSudTgchxKtifbu7uxm6dQ/NepPZ6aucuzLPTtViwW1zpdNAEoIgjnDjGD+X4rt/6Ef4dz/6I5uFgd8Em4L/Kqart5cvnbvMrcUclcYal0VIX0JnJGWiGBqdKGQwqZEwVAxVJghjpGt/FEICBGSSJj0Zk1KrxNzcLNu377ixg3oVk0gkeP9HfpgvfuJ3uH0yz3BfFj+ICPyAZtNmvdphqSV49zveTKXd5h3v/3b23Hob3/+R76NtyGjjW2knsmjZLFgGnu3SWF4ldAXC8Qn8OidEm74AJEmlkcxwy92v43x9icl79jK1Y/vXvS9Jkkgmk//Ks/Hy2L13L4lUCj+h8/hfPcTRq9P0hoKsphJJ0DZ1ktu38EPf/R3c98CbNsX+m2RT8F/FSLLM7FqFEUcghIejwVI9JghhzbbRhITnR+iaQhBFBDGoQsKLo403iAWyLOEFIaou43TaN3ZANwH3veGNqJrGw3/xSY5fOMdAPoGhq1Q6Ie1Y5033P0AoZJp6lrv37ef8+fOcPvMiyZEhnESBwkAfqqkj6QqZgqDY38X6wipzbYfy/AqNaIETkoKiptg6tQtvJMsDb33zt6R/7I1mfGKCsfFx3vbe93D27FlOHH2RTrVGMpVkx9697Nq75yUriU2+OTYF/1XK0tIST3zmcxhtmcfW59lfzJKwYzQ1oqZIXOi4VCKPmY7LfXuH0HWFOAZJQAgEwYZTYsv1uLrcZPedRRRVu9HDetUjhOCee1/H/tsP8MKRIxw7/DzTywt09+eYGhnCTabp2baTfZOTCCH47Y/9BpYckx0cZnL7GKahYzsudsch1FQSKZNcMYfYvZ31xiof3NeLCCQGu4u8eGmJow9/ksNPfpm73vQ2xse3kEqlyOVyFAqFGz0V1wUhBIVCgXvvvZd77733Rt/Oq55NwX8VYts2hx5+hJmT53BKVXRZoy9OsCzHFIe6GC+m2CpLXG50OFJa4/e+fI7//cHt9GQS+FGI60d4foilKTx+eA7Ph/npVeavzjI2PvGq+/j/SsQwDA7eey8H772XKIqwbRshBKZpvhR+ePaZpzGaK6hWglR3EdPQCYMIWQh6CikabZf1lQoDvRl00cMVXyKfSbBcqrE8e5WtKZkjpTJRfYVP/fYJtk3spJjtJ9PdTZgw2LJ9iq7uLgaGhshmszd4RjZ5JbAp+K9CZq5eZebIcRbPXECKYg4ku7kSR+zeOkI+YRLEIXYYM5pKUPeKzIk2f/DIJb5tfz8D+RSGJFNrO3zxwirPnFoichWuHL9Mt/ksjVKZt3/wfaRSKQCazSYrKysEQYBlWfT396Oq/7THeRzHtNttoijCNM2Xnh/H8Wsy1ipJ0j84LA3DkOkzJ+krJDmxWiYW8rXHAzRNAQSKAFMIaisl1k9eIV11mT22gpDhmaVVJicLZAoWlxZrRHaLM6dfYKJ/kJmTCn1WkfUnT9A9PsRKp0lqpJc3vPXNTExMvKaa1mzytbxswRdCDAGfBHrYsNT+wziOPyaEyAN/AYwCM8AH4jiuvtzrbQInDx9l9vh5RhM51houzThkfHQAYpWOE6IpEm4YUPJ9ckJh1RHM+gH/40vn2DqQIwbKNZtG08VoqaRbaXqkNOWnT3D25CmErvGuD7yXw08fYvXiDHI7QBATyRJkDLbfcSvbd+74B+IdhiHPPfssh7/yGM2lFTqOzWqlgmToZEyLroF+JrdPsWP/rYy/xrpl/a+USiUKukQnn0eXZ6murOGPDRLFEaqyIf6BFxBU1gmmVxiIFPLZLEPZNH19OVKSiu+ENKyA7t4sXUM9PP74WbYqOfYNDON2Qq6evUz79Cy5VIrLjx/lkT/5C0Z3TrLztlt53YNvYXxiYjMV9zXG9djhB8CPxXH8ohAiBRwVQjwCfC/waBzHvyqE+EngJ9lobL7Jy+TS2fOoXkDeSNIRCnOqz7aUSVHRaQchix0bC5WUpKFrEpohcyESPHduifnpOrouYcUKtYrPsJdlNJvAFALDj1E8nz/+1d9keX6BYqgR2y1MNkrYXd8nrJo8v7KO73ls3T7F0ReOcOzoIZrVda5evopW83jL9r00Y5mzl2boqbdISxLtKGT6+eM86X0GOZNiZNdO3vu938Ntd9zxmsydDoKAXDrFmqwz3p3n/OoKS3PLZPJZDENHFhJ2u4N7ZYl+M0trcZGUGpPKJpFkCUWWGS2kubBepRp5jPYnWSj20Ky1OVGbxrLFRlOWjkdD00mqKoXQYcE+S48v8/RqnYu7t3HPgw/8qxx8hmH4is37fy3xsgU/juNlYPnaz00hxDlgAHgXcP+1p32Cjebmm4J/HahXa8TEJIVCEMUEEtSCAE32aPgBBdkkIcsbIZQIvDCmT02wL1FkrVqmC4OUq7MnNpFVlZbTppwGR/PRkgZqqcnn/vSTvPP2u3lg316EkPB8H0kS+GHI2dI6f/OJP2e2MoPfWiSX0JAIGTZ1VEvnT5/9Ao3pMuOSgSIJ5jyXKIK8prNf1Tm/ss7Zpa9w5rnned+HP8Tug3exfdcuEonEqyJP/Hqg6zq2H7FlbAvt2OHS0ctU5+ZoVqp4g70YCYuVq/PkGy6t+gpa4NCV0kmmTIIYMmmLTD5NXxRz+FIdzWixp6/IsdIShtsmThuMDhcYkyVqLZ9qtUOmLVFuVnnsyDO8AcG28Qme+cLf8qb3vetlzXu73WZ5eRnHcchmswghuHjyLPOXr3LpyhUWSusI0yCTSXPngf3cd9+99Pb2XsfZ3OSb5brG8IUQo8A+4Hmg59o/A4AVNkI+X+81Pwj8IMDw8PD1vJ2bllx3gXOdNnqnQym0MUILrxNxyW+TkhUKukBFAiGoBx6rHZdUoNJo+XgNGUPVyUgGGUljUbKJuySSWY3+gkkqpZMZ6uVCqcZTp5/i0vxlcoaKKgSmmSCZyoKi8uTxx7htbz9jt3SjCEG9YbO02gDa3Lo7y2kcKksuxUBhTyqPLATNwEcSEgcz3Vxs16l0fD7xO7/HrU8/x7Y9+xgZH6cwPsiu/Xtv+nS7YrGIoxps7ZlgYXmBA31Zjq2tokiC8tWryJJE/fwMeQx0QyYR2kwOd5NOmSyvVEknDSJAxBI9uTSldoQldLKZJF2qxK0T/aQNAzcMaLk+Ncdnqdpifdmjy5I5NPMUFz4zx4MPvIsrFy9xy769/+wxtFotvvjIo3zxK19lbWmd2HGxyyWyksxdO26hUW4jJJWh0KRe6+B7Kk88doQvf/lxfvAHP8Sdd955/Sd2k3+S6yb4Qogk8P8B/z6O48bfj+/GcRwLIeKv97o4jv8Q+EOA22677es+Z5OvxbISnK+vsOYGaDKoDQ/b8cjIJiIWzIcdEvJGR9Ky72HEGx7hmaZEt5KmEwWsxg4VycPoVrF6FfaOdpFXDTRVpuw5DHYlKbXaPHZshrftvB3TNHlxvc7h2WnOVVbZPZVjYijDLVu6SacMiGNW1ps88cI0q5U2e3f2sabXmVSSKKsBW7Qk9cBjptPCCUN8DUZzabrMmI6/wqHDX+HCzCDG0yaf+8xneO/3fYi77rrrpj3kFUIwecutnDvyFK+7+3UkrATKyeMcu3SBmmqQLebxGg1sv81Ij8V4n0UmY1GutLA7DgNdaWzXp+WGdOWSCNfHr8uk0yZdho6hKgRRSBBHJAyVQspEkgRnO2W2JSx2DWSICXj6qYe4WJplcmrbP2hyU61Wee65ZykvLyLJMmOTU9y6fz+6rjM3N8fP/sIvc+7MDF1OyKCRImq3UVoxEPLY5S9hZQvcuWM/3fk8fhhwubyIMmhgFYb5v3/zd/nFXyowOTl5YxbgNYqI45evsUIIFfg88OU4jn/z2mMXgPvjOF4WQvQBX43j+Otb913jtttui1944YWXfT83M4eefopf+rEfweo0yacsdEliba1OR9PYPjLEViuHELAWOrhRSCP0GQxNVi6uMVAWrIcObhwypCQ4rJYobEnw+l2DdKsmPhFNfFAiTEMiqak8cnGBL75YYnR8J7dtmWS51eYKNfoLOt1KhzuGDfq7U8TEmAkdhOD3P30YE5layWXKzOFWOgysCbYk0sx0WjzirZIeSTKUSXK2XMdJmWyZGMULY9ZrHVw3YrXSYM8dB/nZn/0omnbz1gecPX2ahVMvMJDUkeKQ2blZDp84xfErs9TdgPsGxwjrJZIaZFIWSUsjndRxg5C2G5IvZjjl1BEJDfeqTXuxxq50guF8GlmAG8d4UUTHDVhptrgQNvCbNjsn02ztK7JcF1xtCO794Ie4/61vR1VV4jjm85/7LOef+yrberL05rOEQcSV1RJnlmsUiwM89NmH8FyTsUhmu1UkBpqNKqNGgUbgUgkc5utLGOksPckiXWYaSZI4565RHB9mzbfpHuviV379v9zoJbgpEEIcjeP4G1rdXo8sHQF8HDj3d2J/jYeADwO/eu37Z1/utV7LBEHAJz/xCT79e79Od9Fk163b0HUFEQvG3IBzF+c5uTRLs8tjKJWiLQIyloRmS1yYnqOrHJMRWVboUIs8xkQKK6HS15sgr+oERHREQN5ScAnxCEGE6FqC/m0Fbh8e4HKlxGy7RXE4w4HdQ1iKzPTMVbYPKuSSOrW2S9ULeNPdk3zyoRPohslpTSLfn+ZIaYHXtwPOhQ0G93VR6E9Stz1yhT76B3qo1NtsnejjdV05mo0Az4Unjp3mBz7yIf7wv33ips0m2bFrF/2Dg0xfusT60hyifytvvuUefmByGysrK3zsJ34KbWENUDCSLUaHu2nYoJsmeirBbL1F/7YCgSJx9Nw6iuOQ6c0TRREBsO645DWdXtOgbLe5d2yQFy/O06l6zCt1Rnr6uLi2TtptMDc3x8TEBA9//nOsHX+GD7/hLqy/l0llGSarF2d57tGncNoS2zM59qTyWIpG1W6SjGU838VSZCJhICezzNZLWCKBoWUpZPLYIiArp8iECo88cYi1tbXNhuP/ilyPkM49wPcAp4QQx6899tNsCP2nhRAfAWaBD1yHa70mabfbfOq/f5K//vj/wz37h9g31kfW0FFEhBNErDRtBrp2cOT4NKdPTtOx0jgihtjnHjXLlC3hWXDcLlGSO1yRaixJdSJFYU+qG5eQMIqRzIgAQSgixlMW87UOTiJPj6RiEDGZMbhqNxjMJ8glDAxVwekZ4MLiMgd3mGSSOrIr0fIiDEPFlQz6d+3ClKC2WOcvl9cZHzS4b1cvoSExf6nCzqkRKi2bd775VmRFRpYUhGiQkDN8ZOwB/uwLj/Mbv/Yr/PR//LkbvQzfMrLZLPtuvx24HdioVzhx7Dif/vifcPbsKaaEyWB/GqMrjSdJ5IpZVKBcb7OguNxZSFKq2ayHLTS/hR36JFBZaXcYSCRxvIAjpRJRBlJRiK7J3NGf53SpQSttI0lQLa3x2Y/9BoXuHp576nEe2D3Fk0+/gKap9Pb3UMhnOPniGexynU6jxWBmlG5FJW+YRHGERExaVjAlGS+K0OOIhhMxIFuoekSrtU4iYaEJmSiGieIgxy+c5NDTT/Nv3vPN2TRv8vK5Hlk6TwP/WKD1jS/3/V/rRFHEo5//Imcef5Sx/hS33zKOcEN0WUZXVAw1JmXoXCrV2btrmLXlOiMVgedHnG23OG/GGJJCUpUZyqgkhcYHBrfQikI+v1ZG06EqOUSxYEyxaOPTZW2EUBabAWY6S9Bs0HZd0rqKJCCf0HC9AFNVKOTTXD03z21+gKTIJBMmcsVGlgXrDZcDPT1IQhBnM7idCttu6SNVMAmBnmKejuOxf88YubRFGMW4XkQ6pVMpt+hPdvOGu/bwmYefZ2FhgcHBwRu7GP9KHDv6In/6a7/Nc88dIp818HIWF90mw5jQdJk9U6NlyYgui2Qhzd88eZ643kIJffq3JTjplFGWq2iRzNVKhVrgsGM4Qz5pMF0r03Q6lFpJxjJpLixVWCw1Wbp4klFDZvnySQ4kVdTFZVbCVZLpNGdePMOJ+QVSikw6CDAjMISMFELH89kICwuCeMOJ1QtCfM9BhBFNp838QpOhRA4nBJFLgdNhPRZkJJ2li9Ov2YK8G8Fmpe0rnKWlJdbOXSForjMwUSCbz1FZXEVVNBAC4hhZEvSlLRYbNn3DeWZLq4xrBobASHnYAAAgAElEQVQnE8shHUI8JHwhc+tAip25LM3A40yzQbvtMpFN0/QDZBkSkkKPpuPHMa0wpuOECD+ipXjoMkh+RBRG2E6ApUdoigSyguOHJDQVATQbHdYrbTKD25EVGd91aTSqTAzn6epKYeoKbhAhqyphHDPcmwMhEREhywJZkkEK8MOQVDLJlqE8TzzxON/1Xd9zo5fjW47jOPz6R/8z5cuXmNyaY8+uPg5uH+XM3ApPL6xhdedImBp7t3QDglrdppPXuXwp5OyLlxjZO8D23T2cn6tRXrXZ15tl18AgEeDFIWZCZm/S5Eq5jS9krixVyOQTbBsr8MVnz1P0FXKSxuJqBaduU4kECcNguwsvLK/QRCb2Q7xOCxIGMqBIMrqeYLndIYgi7NDnamMd4QaIGOQ4ZrlZoba+TFyyuC++jTV3hbQP06cusLS0xMDAwA2e+dcG0jd+yiY3kpkLl8Bp4YUuZsLaEERV4IcRIF76bJU1VII4wkxbmAlBRbPRkhHjfQbZgoyZj7AyMRXfox2EIAT7CxmuTlfwggBDlvClCF0SxAiiOKbWtlkqN5FDSKkKFdcnKeDqYo04jGjZLm3Hw3c9VHXDnG2t0uL0pVVWKx7b9+/GDkOuzC+jyDE7tg9TqnYwLQ1dU2k0bJIJHUmWkSTx0pfteBj6hsOn7wfkExa1WuWGrsO3mnK5zNGjR/n3P/TDLJ88xYN7tzHalWewJ4cpS2hJjcGhNJOT3Uxs60O1DCZ2DLJr/yg9vRm6cwbFgSxzFZujl9ZYW2+yZzDFrvEcrcij7HRYrDbxqh36rQS39Re4vLaOHQYc3D3Mem0Nai36iFlaqyC3A5aFxCFN4/MInpZ1KorFfMdBEjJpz2HFa+OHEQJQhIRhGCy6NZabFXKRjBKGJGOVfXKBvRQZl9LEtsPi2jw4DtsHh+lVLJ5+6EuUSqUbvQSvCTZ3+K9w7EYL3w8Qik7LjXA9HzOh02l7yJKEdE3whRBIQmKl2sGUBXeNpblUF9w1nCGIImLgatWmTzc4U20yltXJqzoHjJiHj0yzZaTAlp40LSHR8nzWazZypU3FLjFQ7GKp5VN2HTRJZm25zsVcgqnRApVyg6RwWKs0KTdcLs9XmFlsUdyyBcuycOtNLs9U6MqlyWUSlKtlqg2byeEi9WdmaLU3epPGgCQEnh/QsQMyKYs4jllbL2PqOv51tmHwPI/FxUU6rRayqtLX10cmk/mmXler1YiiiFQq9bIKlnzf59ChQ/y33/kdGrMXiVybZqOJomq02jZWvwGSTN1xaYuQgwe34voh8yt1Tp4q0a7U8V2PXM7gIx+8ncd6kjz5pZNEMZTqbS5EsNQMiQ2LIIhJI9MtYKHZQVMkLAkiWaEnpfA3T17kDqtIHAecXSkzU+ilMHULvuMSqxqeruN1Onhry1RnZ8i0bDwhsaxZDCcLuHGEp8kcay+TCaDpd+hXdNRIhzBGCNhu5RnyU5wrr6N2C3ID+5CLOjuy/Tz36BO8/QPv+Zqm65tcfzYF/xWOZhogKaRSGVbWWqxXmnRndOSUoNFy0WKBrsj4UcxircmV+RLv7FLoSmlUfJ2soZLSZGbrNn0pDdeOkFVYsz2SmsKeYobqos8LRxY4kdZIJzXGrQRB08dueShKhJIfo6+QRe44XFytIFyfcxfWsTs+TnmJfb0xz55com371DsR50suIwe3cOzMVaqtiLhrALNps7RWY6SQ4vlTi4z2Fdg1muPolUXuqW2lmEvhBwFLpQYpK00Uw1qlihrGXFxr8G/edvd1mc84jjl76jQXjhwnHUqYskYYhZwLnyM3OsAd9x38B/noAK7r8rdf/AJHn/gqRhSQTCUoDg4xsn032/fs+2e7UU5fvcoXP/1pHvnMn/G6kQz775nAImS1XKEcxByem2etJlEcSbPSjJAtla58ilgR7No5QCZxhYSpMLF1DD+K6DQcym2JVmKAepzA1KGdyuG40B0qdBk6kYipSBGL7TZWp4oKFPM65XYH3IiR/iT/48XznM91c8td9zO/tkpydIJsroAXRgS+y1q+iJQvsnD8RcaaNpfWpllur9NTKLDeqbIlqZGOYjJRHimU8eOIuXaDnJ+gGJtYmkwzimgQU8FlcmKKfCbL3Nw6q6ur9PX1XZd13uTrsyn4r3BGpia5+sJJvAiEkuDU9Do7hrP0pXWyuSQd26bheFwpNTg8u0JB8nj/rkH+4vwKd/RnkK91ttIUibRQuNDoMJRIMFvvkNRl3DCioOvsNOD0coMLTguzJ2A0laS4bZDdOZ2n5mc40yggaQmyukVV8QjqNnMvnmPY8qmGFqsdh7W2RznQYXQPc0EaKZPC6rXotBu0Wwal8jrjwxa9uTSPvjDDcF+GzrE5PvW553nTvbuJIoGMjkDiwtwyXssho+v4WpYdO65PJ64TL77I3LMnmSz2sLJe5vDFI6zUVkFWMI9ZXLpwng/92x/8mjTQxcVFfv2nf5pstcK+7l4SmkF9ucrM1TkaM1cozc9w8MF3kc/nv6l7mJub4/Tjj/Hclz/Lh/cP8fqpcdZrNXB8XNNkPGtxYLiH3zp0hpOn57hlchBHF/hRTNLSAYGV0FAUCVVXWJhe59SF9v/P3ptGWXae9b2/Pe999pnPqVPz1F09j2qN1mDLNh5iI4xjG+ML4ZrhAnclJJdccMhNYliQrAC5K8ANhDCY0RBwbGzJyJIlW7Jmdau71eq5hq75VJ1TZ572PNwPJTuwgNggyTZGv7XqQ+1Vp8771tn13+/7vM/zf0hP7Ce5CitrVdKRwsHRKZyuTTiwEP0YQ1IIY7CkCDsBrVqZzHAByw6YMA0cz+O8HTF10wnq3Tb68AjDhSGcMEITRZKGiZ8v4gYBysxeVhbnUQKbjBExX60yrumMyiZuGDORTKKJEp2Bz4iUYNnu0wkCsoJGNpHEE2E7GvDW0d2D+KyiU6u8LvivNa8L/jc5ExMT5GcniC+nUaWQZifi7I06paRIKWsgiQJbzT7PXt/CaNt8YDbPQyt1EorEwYJJ1w1pOd7/DP1IAvmMyY4XIYkiO55D0/bQYgkTlRNCiflqm3bK57AMK80eyZSEFLVxRA/bCXFaXZxOm8DyGJuZgFZIo2WzHYlYExOMH7uZ0sgokizj+T6djsnq9ZfIST47HYGDkxkUVUbXVN73bUf5nU+fYX29xsz0CCPFPLbjklBkVFHj+Q2bf/6TH31VjLeWlpb4w//y63S3NtjY2sCQQ44fmOTu4wcQJIWFWodL55/kF35+h4/8q3+Druv0ej3+33/zbznlR7zpljsIwhABUBSZm8OIp5eX2I4ucMZM8o7vfP9XzTaJooirZ05T39hgWHY4PJyl1m5jOQ5ZSUOVJfwoIq0pvOPAGH90aYtbD07TGNj0bZdk1sALY9pti+nZIp4bcP16m/zEPp5+ZoEBKaKsgCoEpLJFQsmiF4mIgy5TkogApGSNHcGnIykMGTp236PSsfnvqwt0zQyTRopGs87MzH7CKCaMIzRJRgCSuoFl6LSTKdSpPRxUB4ymE6RrDZJRSDyIGcUgq6mYiowXRTR8l3EjwXynRxxDSw5RzTyzk1MoL/fgFYXXQzlfD14X/G9yZFnmTe9+B81Gnc989kFGhvIkhFGurpZ5obtEq9fD7jsM6wajasBjqz1uHcvwHfuHEZEwlJj5pocmifhhjC4rhEhkk0ni2Ge13mOtazMYRHQGMaaqMS2m2ZY11vs+maEkh/aNsXdsCFWUScgyO5bNpbVtvnh2iYuJIsP5AiMHderrW3hBSO/aVaJOG8VIMBj02F5cwr6xxm3vuoNbjszw5NmzNJsNclkdBDiyd5wXr5d5fqtJIZ9BNQyMVIFTb3gz/+pHv+sVdW+ybZvNzU0e+PSneeiPfx+l3yIlCZzKGaQTOnajxrlzLvfefQvf98abOX1jg3ONHf7wD36HH/yhH+X+P/s09o1V6qPD/Mn5s+QlielEgqyRwMxkuWfPHA8uXqW1tkylUvmqK9RyuczCmTM8/sWHePe+JFHgEBLT6PdxBA1VkvC8AFeROFTMkBbKdJoDMGK2qm0KpTTNnk1/4JDPmVS32oRKluvXyljyEKOzGexL19GGxqm1OmTMLHJKpuF7ZDwPWZKwJZ9sMsHF1YDei5ukzAxWlGLRCwkyaTZ6PXTNQJAk3CgkjHdDYQgCgiiAbqIZJsgNEpkEq60+45pK1pDxcen0fOwwRAQMVULTJHKxzrw2wI5iBFVhdGSETOp/uqR2PJvxwte2Q3qdvzuvC/7fA7LZLN/9f/wAEwf28eu//Xssrm2TTBdQpmdwBhaVs08hO20ODSc4NZHlzvE8yssr4r67W1S13nfw4xgJkbOVOmP5LC+2XC41LNxugOzIeEFAT/HoqzFoEntKQ+TH0pyanCBGQFckojjG1BSO75ugOfBQhvbzfd//T8nn86ytrfHZRx7hhbPn2bp4Acf30EWRb7/9du748PfQvXGFctXiTbe9gVa3zUuX56nUm7R6DmZ6jFvuvZmxqVlOnbqNAwcOvCJLBc/zOH/mBRZeuMiFRx6nNX+Z0cBC12RuG0+xv5DEjWK8GDYGXf78wcfx3xExns7whQtbPH/lBn/yqU9T3qqwrzTMTCbF3uG9OL7PtVqNUq/L/jjCdWz2pfNcW1uhVtn+GwU/CAKuXbvGL/zcz9BbvIiCz1ZH5OmVCoeGc+wdyjCwPHY6Fqoo0Zc9EopCUVWorDaZ3DPEwsUt0ukEW1st8oUkvb6DNfDoOxLrtQApncCQRKQYsqVRrt1oscduktdTGMk8TrPGhKDR8i2Wyi3KnYCbJvdiFoYQByHjYgJPUYkQCV7eqWiiBFGIF0coiHgIhDFoQUhCEuj1+4wk4WShQMrQWIhrWEGEIkPT8TFlGU2WCYMYU1EQxQRBaBOm9K9U2FqOja2LjI2N/Z0/79f52nhd8P+eYBgGb3vH27n3LW/m0qVLvHj5CgPLoZDL8EXFxz3/OEEExHC63OKmkQy2H7JjeYSCiKAZrLRtOqLGWs8jE8fo2UnC6RLiIKCzskHD6nPRadKMfU4UhxBk2Dc+BOweDAuAT4QgCKiySFKT2NxY5EuPP8Z93/Eepqen+ac/9EPUv7POYDBAFEVKpRK6rhNFEZeKQyxfOs/KdpvAixkaniVMjDEzNM49b3krk5OTX1OmzFfD932e+PwXiNca9M5doVDbZjyZICnqQMyNrT51p8WwqdJ2A1KKxHQU85sf/wxvfus9gEDDj0ESKE2OYAURj1W3udSo86bpGfbt3ctqeYuNwYA9kkRCkunW20Rh9NeOZ319nWcefZDnH32QEzS5+20HqXb6FHSBpK6yUO2wsNNibzbBTt+m1nfQJZGxQoLqwOFYQiEq25Q7FZ58+jqHDo9z6y2zrK/WWV1rcPZKgFLcy2QqSbdnk9J0OtaAxNgBLi3Ok/e2yBnQ7fdpOm2WXYsKMZJiks3l6PUH1Fo2pqAwLCvs9Lt4RoKGa5NTNeSXwy1OFOLGAn67SUGU2Gq3uGe/SVLVsaMQnZhcwaTVtdi0baYSSQZWgB+F9D2PmmshRCCaCQJTZaQwRN8acGb5GrkD05w/fYZEKsX07MxXOq69zqvL64L/9wxFUTh16hSnTp36yrUPfeD9fOTH/wXnH/0MNzo2CUnkzHaXmaxJwdTpxRJtFBL7D3DnO9/LvW9/51dWz+VymdMPP4ZTa3H20gXOPvwIk7GCYagIikghk0QSBUDAiyIGgY8fRQixgCYL9OtlPvEHv0Z55Tr3vf97mZmZYWhoiKGhob80blEUOXHzzRw4coT19XWcwcvpkGPjX/Nh59fK0uIi8WYTq9Yk3NlkQpe40bbRDIW8JvPOkRILXo87RtIIwNVmn+WOxYwq8eIzL9BPpZlLZyjkssi+D1FIFEErhkfXVni7ANNjoyxeu84+Rabf6uIEPsm/JlOnXC5z+ZkvsHX1HPdOmRzMpOj1esRxSLnjMSvBbF7nuZU6D1dbHCylCMWIRdvn4Sttru30sGsraKJM0UhwW2qUC8+U2by+A5qIlpCxgjxze9N0+hZWz0E1k7S3KiSKk2SLexE8j/nGJinL5UAEmqLR6W1y09wEe/YNEYQRlVqHdrnF8sYawlCeoCuzHYeEM3PomkYUx0RAq9OGnSpjqRI7nsVQahhRllncrHGraVBMJoim8swvbdHv+SQimZ4dMCBk1RmQFmNajR3qz7s4UkSXgD1TM+TrHnGrykZvnguPPc3sTUe44+67Xm+a8irzuuB/CyCKIr/4S7/Co59/Fx/75V+k2ajScD1W6h5iMyRTGmb26E289/t+kIMHD/6lXOeJiQlyH3ofKzeWye4Z59qNazSWN6m3e4zmM8iyBDEMAh87DJBFEcQYy3XBCyipIpXKFp/77CdZXJjnIx/9j/9LCwRd19m/f/+rOv9Op8Pa8g3sXo9IgBeeOM3x7DgXXnyBkizSGgwIoogxU2MiZZCURaw4pNrzODyU5K6xHJokUt9qE/s+pV6Xvh+RDgTSskoUR1T7XbKaTKQYPLu6ynuzWRLFAtu9PludNu5wgampKeI4Zmlpiecef5L6Rpm15RtMT2Vh0KZQgOVylYwqMpfXWW0GQEzP8xhJaXRdDymhMTQ1zMF8irmuzcxWn0rZYnigkHVFPDvmVm+IhSsVgqwApsyOv8nAWCedzZOQZTxPol/tEPZixkrT9ESRvG5iJBz8fo/V7iZDhSRpM8vaVotkUkeTZYrTWe6aNWk6MZ6ZpN3p0Fg4g5+dIFUYolHdplPZZlLLcHHpCnuyGRqWTzEnIasynuOjJlSySYOTe/LcaPTYbFhYckzLi0hkUvSCPqOKQMJrcfqLD3L4yHE2/ZAzLz3DcnWDvt1HEGKc344wM3nuuedejh49yh133MHc3Nyret/8Q+RVsUd+tXjdHvmVEwQBly5d4trFC/iOzejYBAeOn2B8fBxZ/urP98988n/we7/4H7C9AUcOTPH+t99KLpUgjHYrKp0owHE9mvUe8xvb9He6LF5dJ1JEnFKakcmj/Oqvf+zrsjJrNpv8xn/9Va6dOY3b6dLrDdDDiKjjMymlESOLrCZB4CIJ8O2zu7sOXRIZRBFlocudk1n8KCKMYv6/l9ZY7zvMJXMM5UrM7duLLAhEUYTneqzUayw6A8rIvO3gUdKGgb5Z5gvLS7zrIz/Fu++7j9//b7/F9unL7EvkMWWNWnmNhcoaZXuLk/sM3n2oRNaQMWQRL4w4u97E9kMOlpL82pl19JEisSTTGHiIkcC+bI5+HNPvC2wvbZNacRBjgaZnkUJhSkpxI2hzfXaK9NxxNEnB9EUsIUB3+3RDG0GT0HwPu7pKo1MnOzOJOTrGVGmYKPDZ3FwjIVp8z7vvImPovLSwxk7XwxOhM+iwsVmjVnWY0g0GtoekJOi5HsdKCgdG84gMGMvoLGw3KUoKRRVCzyUKBDq9iNMbLQzRZI8uMacLLPYGDOkaV5s95tNQOjZKqMYkTBVdVti4uo29YZGNDSRULFlmYOrsO3WMH//Jf8H09PRrfm99NXzfZ2Njg3a7ieM6FPJDzM3NfcN2JF83e+TX+eZClmVuuukmbrrppr/T62+69TYa3/Ve7v+zT1Jvd3js/HWOHZrCUGVEAQLLR/FjpDBiUlJxBZX9xXFOV+tcrXawiut85jN/xvve9wEAKpUKzzzzFPPzl2i2m8iyztyBg9xxyx3Mzsz+rSpVa7Uan/rUp7h69SqrN5boL84zq4jkBQHX98n6Ir4jIHgiw0pATwxwQo+UJqJIIpok4kUxQRRDBMgiighOBKIooIgCBCKHMzkCAURiJEkiJkaWZfZkc0SNmHK7wWJlm6yZ4sbCAkPHjvDWt72Nj//27+KcWeA7D9yCKEq0Wi2SxQJCv8dM5HFlscbWWJ+JbAFZkICQsYxB2/Z5cLHOIF9EHRoik0+TVyQCx2ej3KCx3SIORApDeWrlDSZdA0k0yKLiETIlJultV2iqCdx0AU9LoCFRc/oY/R5B1KQvWYgpkdG5/RRSSewYGgObiJjQzOJKSf7wwSc5sW+cVn+AEomM5HPsTyQ5oMSc3rzMaKNNN45wi9PoiTTr3QYjQxlENCpVC0WUuNbv0anXSUQCBc0gjkSGJYmSHHAsqTOeSOBFMTd6A+LRBPtPFBFMhZHxPCldY+25dabtFCdP3kwqYWI7Hq2WTS8MWF3a4aP/+mf5uZ//6W9od7zl5WXOnXuWi5dfYKuyjCCGDHoO/R68650f4Ed+5Ee+aSuGXxf81/lLjI6OEigm+eFRLiwv01AUOkmdqfEiYhyjuB7pIMSqdgl2+sg7fYZNgwOFFDfKVXrtDo9+8bO8613fztNPP8mZs19C1wMSWREloWE7LvPzp1ndWOL40VPcecudX9UFs1wu89Gf/AiXHnuCfCjiBT5zOZ1J00CWQEVi2MgypiWouy4PrFWRoph+GDKaVBCBMI7ouAEZTcYNI6quQy4psuvzCAM/pNx3KUkGOUOjG4JruwiGgKJIxFGML8BELku8ucl8vQHNNh/49nez7847d8NKz7zI+w7ciih+eZUnEMfgWBaSF3GznuOZq01OjucIhBg/jPHCiKoV0MoXObBviumDk4yO5dEUBbvnsrFW5cUz81y+skbOVjGyJtX1DinDQC4mQBWxA590w6O7soBQGiejZ+kLIZ7XQ0y6TO7PkkrkcFJDTO4dp9q06dV6VBouqaEZHMHC9zvIygApKfDef3Qn9e0O5bUd8q7GrcfGCYOI81+6yv44S7/fYUeQ6AQqlhtQSKdIqAlsp4/idri7UGAiYzCmGZxeqXP05bOS9YHNbFIgp6msd5tMz47AWBY/ipmYKNC+UsPbdLl9zxFSSZPAjzB0lTAVY/gCShiy6sT8zu98nI9+9KdeM1HtdDq4rouiKKRSKQaDAZIkkUgkWF9f58wLj/PkM5/j6PFR3vP+tzM5VSIIQi68uMSnP/kAH/7wk3zsY3+AoiivyfheCa8LPrvFMJZlEccxhmF8TaGPb0WazSaf/qM/4sr8Eiuxwsibvw2IOLexzVrdZqSYIvQjyitlnI0GU0HETM6ko6hMHJngnokczazE8s4Nvvv938noZJZsQaPdCcjlE4yMZAlCDdfpUF67iiCISPLuP9LfdHC7MD/PT/3gj+Df2OA92QmiOKYbdrm5kKEbeiREBQkYVhMkJZm0qXLrkMuVrS4kJQxFIQpD+l7Aes9hGh1TEamFNlMJk4YT0HZ8rrX7tB2fg+k8hqLQDjx8P0RSQqI4RggjEpqKgUhSlElNDjOezpIeHaY4Mcljj3yBsVij1+t/RRwSCYPycpd+p0Pk2dxWyDHfbrHTdxjNJAj9kMbA5cV+RP7kBAdvP8DwcA7hZUc8VVPI5JNIiszqVoNrTgtjTCYsGEjpNFYyiRGKJAPIDadI1dvMLy8xYo7TESKa2TaHbz7ETCFHzQoxCjkqHR8xO87hvTkaW1tsVQLmDh9GFGHt8nlemr/G/gMdjh6aYryYZOmldTZqbUZKeQ7PTeBsOEyhI/VaNDyHi9c7HJnOMaKp6HaHWVWlkFAhjjm31aQQqJi6QkISWe4PsIIQOwiIUiqpiQxbA5cDh0ax6gPam11G1AyppAkIiJJAGAlohoLn+SQFkXEzyUuXFtne3n5VHDaDIKBcLlNZ36S6XaFWLyOEA3qdFleuL7O83SEQkxSHhti/d4pMWmR1/Tz33DtHKmnyiU88TadnIYsCU1Mlvvt73sinPvE0P/uzP8PP/dx/eMXje7X5h6lsLxOGIUuLi9y4eAkslziKuLq2QqhKzMzu4diJE+zbt+9busVeFEX0+3263S6f/9Sn6NsD1OES2aEcew7uZeD71Lv7cTptzq9sIHkRidF99HcsoiGD/FSWt02P0vcD+nFMaSJNfiLF0qUyjZ0uoxN7eevbbmHf/glkVcWxXbodixfPL/GZT59mamaW6wvXufOOv+qV0+12+f1f+VXi9Qr35EaYSKS41qpyIJOi5XuMagbE4MYhuiCiCiIRMGnofCaxzVxidyU9mTV4fsPC8gOut/tUApvpEYW267PYtuh6IQMvQBZ2vV+iMCQSJTRRwPd8PGE3Jq2qGq7dp287ZPpdVtptzLTBlUtnOXPmIrdSJBME6IYBsUQ6m2d1dZOUDC0HtjsOaVGl5wSUkhF+BNeqfYypSfLTJYpDmV3La0CId7/cKGRkT4kDR6ZYVGTyCYNTh2dJagpB1yKV1Og1LCQnxl2tUE8FiCs2+sDl1numOT48jCRD1w9wZQVfyDJWypNKGpjmDI3qRXKFJJIsEx86yrXHrnFjdYPJQobpoTzh/oAXn1tAEURKZpJ6NiRqeEwYWbK6hBVGbN+okMpJFEwJT4AFp0fgBMRWxC3DYwjC7rRUQdytvPVckhmdSBUI3JBUOkGj1qTTsDiUm+TLFrACu4VesigiipBQDOw4QvJjyuXyKxb8SqXC859/jIQTU61WWFg6Q0p1sMMQS0wxWpxgcvIwHTdixzVZ2OzRrq9CuEUsuphZg1N37mdqpoTnhyxc3eBTn3mOhGlw7vyXcBwH/VU2/Xul/IMV/CAI+NLnH6F+9TqtWp3PP/0E1e0Vhg2ZsXSCsqTwXCpP/uBRvuOf/AAnT536lmrS4LouT37pCZ749INU5pdp7ezQdgdM3n6CRsZk9LabkDQNWVZQ/BDDGGFodIz69etMTYxyYX2d/XuyFEs5yn2LvKZRTKls9hzCBExMlyjFMTffMse+uVHCICCOQjRZZqiQ4OTJGSrbdR7//OdIvzeN53l/5cG6ND/P4tkXGVV09idzrFgdVBGyisK272GKMjExth+iSLvb+4bj8MygzpuOjlBMGFy4XkPxBVRd5Nlmk3xaJGEIdHzwOzEZVUKXRCpuCAK0PZ/FnTZGKkPk+Ygi+EGAxG5oZhPXLe8AACAASURBVGW7Rr3fRypvMDtRYmttkWTeYN90hmJDRLLqBGECWdNZvlHBt3uEYoipSFR6LotBj/0tg5Su0A/hescjKUicLGUh3nU9jdl1Dw3DkFiSSKQTZIfSCMsVPM/jDTfvQ9cULl5bo7nT4t57DuM0bA7sHydzNkktX2e1vMWpqQKGqdIXAga9gI4dY46kSacTKIpMEMXkikncfp9MPk8mm8LIDFFvWFSqTWaLeUpDOVLZJAsvrTOlGxSHU1jbVepWj7SaRBQi9noS3YqDpqssO10MQ+D90+M86zZp+j5DmoYfxXhxzMAPEBEIgxDH8xHCmCAIIf7yrL9MTPxywC0i/ooNuCDshsnCv6Hm4WulXq/z3AOPMCRqPHn5BV68/AQ3T6RpNgLqrgBuH0NuICgSQULHD02C9CH6johrmwzbET/27961m8X2MhNTRU7cspc//q1HQQq4//77+eAHP/iKxvlq8w9C8IMgoNVqAbsFTJqm8fgXv8jFz36O7k6N+cWLTGjwoRNzjKUTdOKAqm1TtQdENy7yqV/5T0Q/9n9z8623foNn8uowGAz4b//pP1P90nmG+iHHzQyeJlGNbeafW2JLsdBHhlAO7CWSZFRZRCQmCCA9PMSg3SVXSBMaCvl8mla/T+y6aJpCt92hvDVgYu8w0+N5iGL8IECRJQQBJBEkSWN0OMfc3Bjnzq4wPz+P+23uXxH8xYuXEXoD0opCWtOJBy00UcaNI3Rh19slAmRht0ZAFARestqM5zRKhRTZrMFWp8dKt0+iIFJp2MSxzHQyQ8FQSCsyO5bLuWoXyw/ZYxjcaA6IHYFZJybuWkiqiCjLeGFA13Ep93tM5A3aAnQkk2La4MSxPWwW69x46Doz6RS+1UYINETLIm2KmL6CHUPTsdgKbP7o2XUGDsiajJ9UOOgFu41ivABRlhEliQjw/QBFEgkFgSgWGDgeCV1HkSQ6fZuJmRJCFCEJIkJSRvFDju2b4P6tKjNHxilO55mdG8UXYspnFtkJQkaSCURR2pXWOCYMPOqrK3SXFul3GiQNma1aEycICGNIaBr90MOzPNKpHEt2j3JggRuiRhLJTIIbto8RwaySohd6ZDWJpudzOJvmQrvJATONKEDf96mLIn4c02z1yVT76ONJyptNkoZCpmBQL7coFXazqeI43j078UIEJFzPwzUTxKrA5OQr6372wlPPcP3iS3x2+zq9XpMTE0m6tkW16jCh5ZjKlFAEES+OaPX6uP1lKrUuRnKcKLTQkybdZp986S8XChaKae591ykWLt/P1tbWV66HYUi5XObGlesM2l1UXWf60BzTMzNf113At6zg+75PvV7nmS89wYuPP05/ext70Cc2NQrT07Q2Ntlj5KmsLlLUY3KqxGqrw3PbNVAUJoYzFEeyuJGAatf441/7Ffb/l1//lqgA/NPf/zidpy9zxNeYGi0QBj7Ngc1ELsuYVETZ2eTSw08hmAnMkRKmquAFAR4hsqrRrzfRDRUvAj8CVxSw/JC67RP0PBRr97BtfGKIVt9GUSS+fL7mBj5iFKEpMpm0wcR4jvMvnP5rD7g8z0MWBIhBQiCKoSDpVJweeVUGBLqRR1ZWqfsOOgquFHA0k+SxlRrH80PcnhmhI3hoxJzMFDlbr/FkpUNSErG8gI2OjS5KJFCwPEh7CepywKQEnhdi4eGJAZoos2nbrLkDJg/NctfBvTy3ss2BQol8yiSX0LicnWel22NS17EsG5mIobTOoDUgpSo8tt2mH4Xcqo4xVswiqgrn61Ve/MI1po/t4fit+/AtF0VVQYBIlAgEEdty2NysE8YCmelxHn9pgfFihv37xnFGcmw1OgwVU8Rdn+xoBi2tkU0mSCU0ZElC1WVuPjDCwukGghAjSbsNbrYWb9A8fZW9owfRZI1BN0BUZFY2alQOtmnuG9Dr2CwslZlM57Ain4EdsRD5yIbKqt1lTydmTMtwobGJK4fM5QxUWeBat0taUdAlgYvdBuebHQxRZjRh4BHxxlKectPGy2qs2w3mDoySHk2zvFRmyrIwE7se/mEU4jghoqAyIGCr32X60J5XZMNQqVR4+IFPM5YVecvtB7n60mXuvGmS+z//ErcWRimYReJYxPZcDEkkl8kznkqzffUKtlYklUrSbgTMXytzWz6JIIqIX3YnjGNGxwrkhzO4rgvsLrCeevgLxLUeo6kcE3oBx3VZfuR5zslPc89972BycvLvPJ+/Da+54AuC8E7gVwAJ+O04jn/+tXifXq/HuXPnmL94kfXlFerlTSoLNxCtAUOqwtFCEUWWqNU7LC19iQY+qwQUNJET2TT3jBc5t93hkJ7EiGVqVY/QkvD0GFcTMNw6D/75A3z3h77ntRj+142dnR2WnnmBUtfDVHVq9QqiGOGFDoM+SHqC6USG7X6T2oUrqG/NkTJ1IMIPQ7q2QxRGuJ0eQjLNzsDFGrgMHJ+65bNHT5IVNfq2RxiEqLK468EDiALECARhjO16dLoDErpOv75KtVr9K/nVxZERYk3npa0yVzo7WFFAPlYZNTQSeYNe6KMKInEMdhRS8RxEGda7NpmezMGpDKIskNU0GpZNd+BxOF3AjWEtsqjYLr1uwBvEIke1PMNygjVxwKbT50bHpmAoaJLIlu8QSDEFVcdUDEbGSuwrFLi8XeXg0BDVWg8zKfKWd57gkQfOUatKjOsGIiKaLLDS7XGt1SQQY74jO0V1EFGNXQwtouQZeJ7IF373aUqzYwyNDxOEMWEUoEgCkRhy8fwSluMhKypH7jiKtb1Npd9nwnMRFBE/DHY7hUkRZkpDVmSSpobTGdAbdkipOrOjeWR3ifrmCgljjn69SXBphcnEGEnVwOnUmZwYo7a9STGSOfvgS1w9s0gynWBxpYowqdAWVa71LT74E/+Mu99yL/XKDo8/9AjzL12mZ3o0m1VyWoaphE7S9/GCgB3X52qngyGLpDWFmu8yljTZ6A64umMhxzHpsTSXLpUppnSiksiZhcucmNqPImtYA58wFOiFA9bjgFoi5md+4HteUYbOo48+zGRW4+iBCa4uLZIyVVa3aoxEKqVkitV2m2arT1pUEIjpxQH5jMmMYnKpU8ZMjYHnsLZY4eDRcTw7wAtCVF1FUWXCIERP6Jw4cYIwDHnq4S+Q6UZMTu4ljmO2t7dZvn6V6uYmjW6bRx+4n/f96A/wxre8lXQ6/dUn8Ap4TQVfEAQJ+DXgbcAm8IIgCA/EcXz11XqPKIr41J/+KZ/4rY+xs7FG2O1TkhVSssw+VWMmkWJh0OULS0vMptKMmAZ7ZRWnZXHdbnPvzTOUNJWnV+vsyxQ5OJRHALqOx7VGl+lkmvlWB1t3uXrpLN133/eafyivJVcuXiKudpEsC8NUyGRNVFli0B9QbvWIpBDRCRmOFXr1Dr1mm6RW3K22bbXZuX4NXRJorq+jjh0i8ly0KGS+3eNUOk9RM6hZLo7jcuXKGidv34frh4iiQCSKiC+vMJuNPttbTTrtLqUE/MYv/XsOHruNd973nq+YanVcm5X+GgfnNG6bKWBIMvPVLhfX2pxdr/Fd4+PsMVPsBDaGKLHjOqw7fUalBCdTOea3m0yOpjFUkcm8iZc1iKKYdEbnoVqZQxmNhdBmzDEJgG7koyMzp2Qp6Srt0KftuTgOTBYT3Jwt4noWVt8hiEJkUcBQZAwBqv0+SVPj8C0TLF1Zo1LtkJEUXC9iPqwhS/Cm9ChZXacWx1hGGl83sKIBpiGS3tnhM7//GN/xv7+doYkSiDL16g7rK9vML27R2Olw+PghkimTyMlgRja1WhfCkKymEEUxKAJ9yyElyeSTJnYY8NyLC9xx1xFcVaWUV7HsDtW1JXZeuEqmJREq4PfrjE2UsG2La1fOk3Q9RhIp/J5D1W9DFCLuNZm4+STf95H7OHjw0O7NdAze/La34nketm3zyMMP8/RnH+D0xjqW7dDt9YgFAVEzWB1Y5AWfqUyGppEgMTrCGw4fYc+JE2xsrfLc809y5aVF4iAgaru8uLHJpJEjkUhjizJNTWDs8D7+3U/8GHv27Pk73//tdpvG9hrD2RQb7RqKDHo+xcryJnvMJFfLFUzf4FiiiPnyrtMLAra6XSzLR6CBlUqD3qdX8zj31Dxp0yChaYgIBELMIPAJfJFisUi5XCba6TI5NUccx5w9/TznH3sUv1VDjwM0SUQbOPzyT/xf/NmJm/l/fuEXX9OK4td6hX8bsBTH8TKAIAh/ArwHeFUE3/M8/vWP/0uevf9+JNfDFARuyRbYn9yNGQ7CgGcaNUxZ5j0j47hRREnXUU0JMY5JaSKRF1L2bHIkGE4ncKMAVZRIagpjSYOdjs3ebIbqdh2kkLW1VY4dO/5qDP8bgj0Y0K5WOG5mGMqYuJ5Pp2vjui4pMcYOQ9KKQNRyiS0Rd2DRaHTYvnwFyelQSOvIsoA5PUxLiJH9PiMmTCVNEoJCz/GpRzZyIPHC2UXGZ4fodQeYSQOECD8I6bYHrK5U2dyo4dYH3H14jmQ+Q3P+HL/w3HP84+/7fraqFf74l3+an3jXIU6MppCiCENU+EeHx1lr9vjYs8t8/MoGt6Tz5FSVbuBxoz9gIPhMF9OIioDmhSxsNpkYSpEyFAxF3rVJaFhoHZ8De9I4CZG9qQRbLZdtx2dEMPHiEBGRYTmBLsiYhkIrsPHjiKyZot6x6VoWQRDTGzjk0jquG+H2O8zlDN7zgdvpNdtEfsDFzRbLD9W4I11i4ARUOy0kUSSwLUIzha8YJIvjCE2JZ87c4IHM85iGhG+5DOotHMvDDmFsdoxTtx9hYLlIkoyiKbQHNlalzd5T+3D7DsQRK8vbZE2dQIFm32GylKO+2WZoOMepg5N88cwi6wuQ3vYZ23eMRMokjgVqlQZnTz/OmBNxojBGQTXYarYZFmU8WcBaG9Aa7jI5+VcLnlRVRVVVPvDBD3Ln3Xdz4+pVGpsbEIPl+2TGxpiZnSWOY5r1OmIcUxob+0r8OooiPvzhH6Hf71Or1dja2qLZbFIul/E9j5HRUW677TaOHDnyinPva7UaOVOhO7AJZJ/xsQxpXeHy+QWGFIFipDJkmEiyRBxHu6nDokgiVtirFlhqrSFbFgXJRt0OWHM3mZkbpi8KRAIIkoTnR/S3bXRNY/nqPCOpHABPfOlxXvjsn5HHY1RXySgqVhAg6zLVvk/5/LP8s+/9EB//84coFouvaJ5/E6+14I8DG3/h+03g9r/4A4Ig/DDww8Dfqnqu3W7ziz/90zz1if9BThSZSJiMGwlkQeByr8WYbpCUFdKKwu25IjlVwQ0j2o7LiJFAE0VuyubY6dmUbYdTe4eRRAHLD4jkGF2UyZsqq5UBuqmTiBS8IKbdbb0Kf5ZvHIIi03I6hFqK5c0KgR+gKSKGIiPLEr1On0BVcQIbp+/jXL3KerNJKpdCzxfo+yFSt89MKUO91uLwniGshIYhAgiU+30GckhQaaDFAU89fhmimJHRPIgitu3iOj4rS1ssXVmnJGqcW1rktiMeYzmDenObf/cvf5h+r8c/f/Me7pgr4fsBruviBy6KIDKcNfjeO2YotwY8vlIjoegYgsB0Ik3H7tL3QxRRIp+QyIkR/eaAvrDrzR6HEYLtcVMxSxRHKJJAWlfoGiEVe4AeymiCTMP1yOu7PjqqsFug5UURcgxRINC2HTRZZ3OrhpEYRSLGs22OzJUgjmnbPqIAchyjBVDQFQxdxvJD0rpCQldpWD7r3RZOXSZTHEWv1Lm20KHQ7zMkSqRFkVEzRWFmnJbbp7q6TXq0QLfbI5WTWVwqM5s16dk2qxtVXrqyzvX5TUJRIN/IkjJUhnM5hlNJsANyooze6+NsN8j0klQ31kEQ8a0eO/Uyya7FAWMY2i4tz0YJQ3IZjc4gYI85Tn2rzYXz57nrnnv+xvtrfHyc8fFxgiAgiiIURfmq2W2iKJJOp0mn04yNjXHixIlX9Z7/i0RhiOd5RImYfMFAcGLGh3I4kcBmpcddhTF8PyQMA0RRRiCiZ7sM7IgRLYnsenjdCrNjaY6ODFNtD+itNjh4eJwohr7v8fnnr3Lt3A1OP/o4pdExRvU8y8vLPP/g/eRjj8mkhgxULAdDEpkyNbqSSTol8MT2Gt//oQ/wqQc//5qkg3/DD23jOP5N4Ddh10vna3mNbds8/MlPcubPH+SImeSeQpGSopNTVVRJpOa4XOq2eLRa4e7iEDMJk6bvkn65oGbCMGgGNsMJDb8bsmQPqHVdJkoZHDfEjyKIQuSXU/16jodkmEj+bpeov69EUcT1q1dohT2WahX2JpIooojnRfSjmEiWMFIGYujTEgb02x7Ook1ici9jBw6i6xqmJBKHAa16HUWwuLBSoWu5uDsOc2KCpVYLKxXyXe84zJXNBv3Q5clHX2RipsTERIHBwOXKtU22Vqt0Wx0OHp/i3QcPcerANLbn4Vld6CdZXrO5a98QOVMjilUGrsLA8YhiCKOYsdEstx0aI3tkmg/+kw/z0tlzXH/+DKXtGuuNHmt9l7GUTEqWUCWRjC4TA5d3emQTKgPJo2CoyIZNN3RJahKyBP0gIC2reH6ErYQYosyG1yalKUiSALaNpcSs9SzGk1la9T4L8jaCLjCqK5iawtW1KlEYkhIgarZJ6TIpQ2Y2ncTxQ7a6Dq4XMJ5JkFBlljot+i0Rj5BJY5hSbpYULgcPTBE4Dq2dCrMpk9a5Ndp7evh2l/a6z9LlNYrHZ3niiQs8+9IakZ5m7/FbkHQT2/Yob6xRff4G/ziVImnqKEjccfIAhrzK4HoTw9/G1BWMnEp7x2ZvZFIKRDRBxNA0BnFINmVC3CMa2OwZn+G5R7/0vxT8L/PNWrxoJpOsblc4eSiFaohY4W4nsUPHplh7aImB6RMFLn4oAjIDL8L1wVQ1tloVcrLEvpSLt9WkrKrsmSxR7lkIgxDLc7lwaYX4uU1uckR+9z//Km/+wHcyevJunv78Q+hRgCkKiMQ4UcyxQooxUyMmptt1ODlhktZkntpc5Jf+47/nJ/7tT7/q3jyv9adSBv7i8fPEy9deEctLS1x95jmKQcSbikMcSGUoWxaiKKCLEuOJBFlVYcdzEQBJFFAEiYyi4vd7nG81WR706foesiAjRiL1gc3qTo/hjIEqibh+CBE0Bg5+KLL36Eku2F2GiiOvdPhfd4Ig4MaNG/zer/0yG88/hal7LHda7BdNRk2VKIaW7VGLIHI9LvRqKCkJU9aYOXqMoQNzjA7nsByXsGchyxJmaYiqJHFjY4H9UyUWWxtcW91GciNuOjJBy3Z5+11z5NI6V1caXFup88KNDdKGwp6MztSBIpcWd7siHZ0ewvcsTr+0xGhCYFOTODiaRpVibNtG0xRSuoIkivjxbmwUQeCWhs/85S733Xcf9913H2EY8txzz/Hgf/0D5q9dZ7XSYm9WRxZFoq7FwI8YTesMJzWe7/U5njMZL6nMr/SYJIWmygzcACsKkBCp9R18JWKdPm9NjGJKCu1Wky1JIW76JPMRaTNDtVxnsVXh7gNDXBBj5tcqjMQhcRRQkGDfmMlOzWG/lCaMYkbSOuWuQxz4ZFSRIRU2G1t04hbTnTaqbzN2cBZDU4lECUuWySkJRKvPEw88z57jwyiaytK1MqsXVkgO5zh44hYmx/eAJGEFISMjSU6eOM7y4gL//XMv8lP/5zvJZhJIssTxuWl+5zceYutKjYwUsTeZRm7a3J4ZJ6moOH6AG8a4QoghSRALyILISDrHi2vXCMPw761t8ejoKBtbVd5yW54wEHj28goXujaxE+HKAk4YI/gh3U4PQgE3hoQi47ptWk6Xt0ykmZzQubrWoX2lzAurTWxiHnnhKkkfMm2PNyZy9HMB/k6bK088g1dpkBl0CVwHw1RJKjK6KDJuaggC7DgeE1mV6XSCthtQtlxq557l+eee4667735V5/9aC/4LwD5BEGbZFfrvBv63V/pLFy9eZGtlmT2mSUHV+bLjpylJCMLuE9SUZIYUnTCOEYGImJbnstK3mFKSHNPztCOHk0NZljt9HDug6rj0w5C8rmB5IeX2gC0/4u1vvIu61UdKZb4pnPr+NjiOw5OPPMTzX/gc9QvnGManUMqgjkk8ulRmv5th1kiyY7vsCDEL/Q69tEBUNPBjndhME0QRQRSRNXXsICSvSESigCiLbLTqxK7Fvv0lxITOernBVDbFRs/mwEyRdFpjrJTmDSfGCeOY1dUmG8sNrlzdZkYxWLte42POGYrFJN12j/tuntz1Xo9jNEXCcT1EIhRZQVcU7IG3m5+uKoRRzG5ewC6SJHHXXXfRq9TxFk/yxT/9I1YHfVRJZG/O4I4DRcIg5KntKvvnckzOFImTOquNdRa6A5IJHduxafouuiAxIKDsDIjliMu1Nl4csuFZ7JWzhB2XgSLhByJVR2Sr5XD2hUX8gk7k+9xxZBxZUnlxy+WmyTRPNNp0Aw9VlFFFiayh4guQVCQyisC6VWUsaTDqRhgpaFfqhK6D6Az+f/LePEqyu7zv/tx9qb2qq3rfZ6Zn18xoJI00SEiWEELswtiICOMF7GDAsX2SOJzXiZ3DyRvbiXMwiWMnMYvxiwEbEJsEQhJCQrtmNCOpZ6Znenrfq7v2W3X3e98/WuBgsI1hRljm81+fOl3nPr9771O/+9zn+X4ZKKZRJAnJd7ht5wDZiQFOnFliKMqT7jeJ+rooZEcQQolmq4NgqqQSScIwYmznTtbXKnzmi49z03W7kQSZ1XKdnoEu1ifL5PyQEd1gXtdRXjQ6iYlpBR7JlIYXRvixSEJTkaRtx7N/qsJgPwiSJCEKIp/8zLcYkmRKEXh+QLnRoulYPFF3SYYKfYqKLIImgmW7rLk2aVPmwpbF6XaVvZkUo4kkCVdipWajuTGvLHUh9m3/OLYCjzN2g6X1MnMyjGkGluvS3WXS8UOG8waisF0mXPI7HO5LoAjQl9BQBJG8Aie/+QDXHT9+SQc+L2vCj+M4EAThfcB9bLdlfjSO4zM/yneGYUhtcws1jCiqOqIg0AoCkopMJ4zQRNBFkfjFm0lAoB2ECDGcrFUZkE32pjK0Q58lJ2DdcxhIGrgRRJZLNREjmwotIsqiyk+/8Y14Ycjjk8/zrv/w2y8rmYUoinj4/q+xePpp3NV59iQlrBAO5hPs7s9zZX+Hr0yt8+jSGo0wRiukyO7sond3H42Wi+ubxOkErSBEsl0ygbw9ESmJ6KJAb0JnM1sgj0el2iTUQrJdJs04QFckKo0OhqGQSqpIssA3Hp6m4Ivsz2XpZFsc3t3LI8/MUUDh1OQKYwNp5tabRFHE3GabquVgKDJhGBHjIwOKJOB6HoqqcHq+zOj+V35XzIIg8IpX38w3va8xcPQaLnzzPrp0CT+ImFyvshY4DI2aXDfehSBKpEyd8Yk8z807rFVcgqTIudoGmVBGi2Q0ScSIFS62WmSSGq8f2YFIwGMLyzQaDlUZFt0aCTq04w5mQaM7n0BWJOodlyCCwZzBrUclHnhmjVEhzUQujaFKOFHEZuDxtFPBynmURI26tcGWBb2lNF5VJJtPIrRDujImXcM5zq56PHX6IlOnVjkyNoLQn0XvGyWXKeI6DpomULUsWpaNoshEUUR3Xw/fum+K8f4eSrksuWQaI5ti2muR0wyWOzYFU2Pds+kRDDYcB1EVSekaNdtF0k3MbJrNZp2BXWMv64nzKIpYvbjM7tDByJnIisCOTIJjhSTfclYxWyJLDZszHZvQ335/M5AwuTJfQJdFnmpvkgwMNtoB/Qa045CELrJTS9Kt6rTjAAkBXdXZm0nhtNoUShmeW1lCcWxkqUBAhCkLbLo+K0GHPX0GfaZKEG/LSUgi5BI6W7VNms3mJXGB+zaXvdAWx/G9wL2X6vtEUSQWBUzTJGq1EYC67zFomDR9j5yiIogigiCQU1Rm2m1aYUAn8LG9iIOZJE4YEsQxh/MFHipvoCoxmiHhCRGV9W0TjMVOyNjuQ0zPLvPc2grXveUNXH311ZcqjMuK7/vcc++9fP6ee1lYWqZVq6C2GxTaVfbnE9Q6LpMrVUZKKX7hqhG+mN7gQibN/usOUHc9Vus2HdciVhRKgyVq5TqqaSCbGq1aEzuOkRHQZQlRgNAPSbgeiaLKTMej6nqUcglcx2dmqULS1JiZ3STZgXzGoGU55FI6kiiQkCQmCmnkIGax2kLpTrErneCx1iqffWaRW/Z0M15KEYQRguATR9u7zKmlDU4vt/jD3/uX3xN/KpXiVXe8nsHdOzgx8zxrSxc5v1bn0FCK248MMFhIIQiw1XKYr7bJdxcYzSeI6xHZRDdnPvUZnMhjSEuSkDUUSSQZSawEDb5WdtBimWonJnBW6QCiGHLj3iKzFYFn1lvcZKoImoIYRkiShBdGHBnIEkQRj07XmG22cDsRVhySL2mMHjSo1ApoQQI9FIi9ANdqsHO8F80QiVyHrVrA8nqV03PLPLW1yWvf+LMc7i3xxMVJekq9FHJdRNG2/eS52RmiGAzNJAgC0tkimVwRPZHDTOdpOS7nZjY4NNrPtf1ZHp2cRXRj1to2B/Q8qiyRNRPUvQgPDTmXIlcq8Wxlnht/4R0v/QV9CXn8scfo9iL2pDPEgc+opiC6IY2GzZCssoXPlfk0U1KLwBU5kMpSUHXsMGTaqbMznaKgaay5Ds9uVBnVTJqOj4uP3QiJCBlUk+Q0BS+KGTUMHEkglU8ytTDHYy0dWRDwLY+erM5VhSRFQyUGXM+n4ngoooim6SiyQhiGlzT+f5pvVv4eBEFgZPduLjzyLeqra3SiEC+KcaMIVRSp+NvTbQlZJiHLrDgdiLdVOYqKTkJWtp2bZIFOFJKQFExTporNqtfk2a0a/XEaM9GLYfv4msrrf/093HLrq77v8dTrdTqdDr7vk06nSSQSqKpKFEUsLi5iWRbJZJKhoaGX5FHYdV0+2rJ3FQAAIABJREFU8Du/w2rbp2//QQpjQ3gXp0iGPn69AeUV0pJMzXI4bbskNJV1RMbG+1kuN5CKOXonivQeHOeFpxfJ9xRwbJfqZh1jqEQqk6Bju4hhiBzFuM0WDbvJwZRCbCgIYyrza02yBR0vCBntztFsu6wt1LlusBtdkdjcbFHImCytNyglNHpMDSdtslJt8OjZTSQ1w66Jw9y3Umfe9dibr3DjqEl/3sTxA6YXt/jYw+e55vXblorfD13XOXTkMH/y2c/w//zGe1mdOsmC4/HYzCa72y6IEk0/YtOHdTuGriI/f+ed/PG//13unDhCYLlIoU1KEgmDAClMsNNL8WRzi1nXJSXrSG7MuJIkECLOL1YY3ttDq93hm3MVRgspskkVWRWp2gEXNi260wa/8eoS1Y7Hl8+usmeoi73DXdx/doWJgR7agcqAZLA8vUrccTl3cZ3+7iwoCqEPHTdgvunw+x/+Xxw+fJhv/vVfEfjhdqJHQHpRlrm/1M30yiK6pmMaBrrroao6xd4BCqUu7r33AeYXNvm5Gw6wM2lQMDWenFrkwkqVFzo19md6cQSNSJCwFCgN9nC+uU7hmj1ccejQZb+GLxdxHPO1z3+JV46MszB3gWsHMpSyCdwgIIvCoJ5gQW4yVW7iiSEFQyUQYi50GnhiyGDWYCiRoOp49Co65xp1Zv0WSVXmmlIGSRKo+x4bnTZnaz6uD5IkUJAF1g2NoWIWQYvoT2kc6U8xkDEBCMMYJwqp2h7rHQ9BlJDTWZR0DtM0L+kavOwSPsAVV17JE/1fZXH6IjXfQ2V7oUUgISt0wpCK53K6UWXJsrB8n6ysossyThSyYnfoSCGRJzDRnea81SSSBHRFJp/W0cd6OXrrWzl2/BXs27cPTdNwHIcoitB1HVEUWV9f5/zUJPPzF3nhmacpz84S+T6pfJHeXftwqxZm28eUFNqhj9Sd5fo33M4rLvFLmL/Nh//4j9kKRV595x3MnTuHM7PIodF+bMeG3i42U0kmFy7w9rECT2/UOblUxe7pIvZDonyKwZ0D2H5AtjvP/NkVykvrDO0eZvr0NPMXFtk53o9saHRaHZqbNVbOnmdvKWJ0uI9p2+eGo4MsfO0ck9MbDPdnyKUMgiBkIJ0gpavML1cJ/ZA4ipk8v8HN4920LJd606bVjGmXChzbsZMgjvHlTebWG5Q9eGqlwoi2wHrDoSKkeO3bf5Vfec97/sH1KBQKfPjPPsFXvvRFPvOJj3D3zEXk6QpJTUFJpMj2jXDj7W/i1bfdzqf/4hNkGy637LgSPwo5v7bG5NIcdFzwI9ZsmzAUmZBy6Kh4sU/dauPKIRP5HnqyBfLZDCfOtvnK2XX68gZ2EBB6AYf6suzPmiBA1fboySa4YrzE9FqDGTugZ3+J4d4i/nSVXcoQixfXILTo6ynS29fH7MIGFipvv+vt3HzzzQCkentJTqqUy8uUCt3fiTmbStOXL7K0vI6RMKisl3Fti/WNCt94+BlmFyvccOuttHWBk60q5VimnM6QSnfTbjqcshxMzyeZzlAaGaTab1Lcv4Pbf/pNL6uS5t/GcRzqi8uYmko+Y2LKMr7v49g+GVnFcj3SisSB7iQnKzWiwGc9aJFL6PQoJllFIaMqtDwfQYBezeCCV+UNQyWShoiJQo+hYyV8Hgo2WWy4jGRS2M0GhbE+xPI6fuCy0HT4JhVuEQUUScQOQmw/our62GFIrOtUY5ndR6665Do7L8uEXygUuOXOn+VPz57h9OIKu3QDQ5IIophmECAJIgsdi/l2m1t6eklKCg9urpOIFeY6FudaTQ735YgVgZOVMt1pjQOlDOWmQxxDoxPjNptMTEywurrK7JkzNDfWWFlbo+MHJLoK5LvSzE6f47l7v8qIIXFjMUt3Ks3U6gpTX36BtpDizpveyq7ebZGnteoWj/6vv2RzdZ03/8xPX5Z1aTQaPH7qNLfcdReiINJanGdnftvCDwF8INPTxdz6Gs+s1jlSSlO2XNYFAWSJgYEiruMhJ3QUVWLflWOcfHweQRApDpZYPTdPbXULq9mhU29SnjzHPrnD7sF+Vrc6dI8UyCQ0rj0wxP2PnONjdz/Lzcd2UMjoVGbqVFeaJFSZtCpzerqMVrWxsx2KhQSxG5HNlrBFDT+O8dyAFALH+nMgCJxYi/nS+U10TeLur37pHzWzoaoqd/z0W3nTHW+h0WhgWRaCIJBIJEilUsiyTLvd5tmHvslVqRKSJBHEEYmUTihFKLLMbKvCuJBjQk5Tw0OLFXIolKUOXw+XCLIq/Yq5LTg2PsGJ+TkyocTEjhK1usV0rc1Wy8HQVeqOx46BHA9MrvDMZpu+q3Zy4Pp9ZHJJnqy3qa5UKA0VmKtb1No+Y+kUtlnDLfXz1rv+pqRy4OprmDl7jscnn2V4cCep5N9MgPd0dRHEsLS+xlOPnSZyKtx7/9PsPXCQ//jL7+Xw4cPEcUyzuf3ORFVVVpaXqZY32arW8DodNEkllc0wvm+Cvr6+l/XLWtje4QuiRLXRJGNoSKKwXRXwPHzPhyhGiLbN2lOaTH8ygeAp7E9nqHgez9VrXHSarLkOdhTQ8HxEJWZnzkQXJdpeSNvbNtrZl8pStjcJBNjstBkdHSLVtImXLpKMIp7datH0Qg52JTFkGTeKiWKYazloO3Yi9Y1w5Ni1l3wNXpYJH+CaY8cw/9sf8u/f934W5hbokUQGNRNRgJm2RRjHHM5kKWo6dd+jqOt4QUTFdSmmTTxVYt6qcrAnTS6lstlwaLRFsokihqrz4Jc+w+e+8hl2j4xT0FIkBY+SCknH5sQDcyxVm+wwZH5+oo/upMlWx2W1XOFw3uBgpp+HZjf46H2f5M6b3sKBwTF68128JpHmy19+kL1XHGBiYuKSr8ljjz1GqruHVCaN1WhgeA5SUsZx3e1aoAi+IKAWSqwsn2fECRhOmXxxvcL4tQfQNWV7hxHFuI5Puphh/+Fezp6eYW1GwndChHaHZqWGWlnnGs3i0EQfvX05HjyxwGsOD7JZt4k6Du9783X8z6+e5vP3TTLSmyUZifQMlqg3XSbX6qTaIW8b6WG57XKuVWHWFRCSCaqdgG9NrbE3myQjS1h+SMaQyRkGff0D9HUZ3xGl+sciiiK5XI5cLvc9n1mWRey4FIw8K1aNlfYGWuwykVaoazYFI4ttRTRaHnK4rdMZR9AjJJjQCthhTMv3ySs6mq5y+MBOZrfKrM9WGelKUBYk7n5+GT8MMVWF5JpFz+4Brvu5n2J0Zx+ZTAIBGNrdix/5tLY6nO00mV8T6Msf5/rr30yjrXzXDrunp4fXv+MdXPzPS3zl3rs5cuQYgwNDeGGIZTvYvk9tvcaNN1zDb/7Ge+jq6vqu/xcEgWw2+52/J3bvht27f6i1fTmg6zqF/j7mnn6K8SSosoIbBIgCJESZQAgJ4hAlFiASaHgBTiegTzeZsSzWQoe+tM6BrjSqKDJrWUw2q3xhcYOD2RRxLOB6ERlBI6cpXF3M88DaFrZRJBZ1jt1wI1svpNiYuciEqrJUt5id26JkqogIrNs+ViLD7cdeyWve+rbve53+qLxsEz7AgQMH+PR9X+N/fuiPeOiLX+Dk7ByaF3A0lyMpy6x6LgueiytJ5LI5LraaLDseyVDlQnmDo71p3EhgYdMnZXRxIdhkTWxS6hXZu38Yq+MwNz1HSpHoy+fYX+ylKUekhrpYUSKu6kqSlUALPMZMiUE1wemyxUguyXW9WcoXy3zjxKMIMRwYHkfXVPakSzz69QcvS8JvNpto5rZHbLttETttHElBUQRUZVtvRRQjZE1hoePS1Wjj+hFBo81yq0Nvx0HUVfQ4giBEM1W6B4voaZPpZ6epLVUwHOgviLiRQLsWUexOM1tuEeVNnj63SrvtsW+ol/l6wNXXXkPDCkmEEZu1Os9sLbEnaTIhyfR1JRBFgeGETrvaQvQUytUOQ5kcHRc8y+d83cIKQgYSOpEfkU3nQJIuSw+4KIoohsZitUrCiNhlaASewLzQxBAkxs00si4wrVlsbPiUQhNBiBFigVKos+FLzHQszKRClFM42lNk/44+ztS2aEk+YzuG2JB1xvf0s+4G3P7Gq+jrzRL427LOAEEUsrFW5fCRceIo5tTkLKJtcsOtx9F1k9Nn1r7nuPv7+/m9D/0Rn/rUp7j7i1/myaefJpHKAiJCFHPdsSO84647vyux/6QiiiKvfMNt/J9vPYoa+/SpERIRuiKgKZAWVfzAZ6pu4xLSbLtEvshcs81yZHPjSJGCpuLHMXboI6lw23iJ6WqHGavDLX0lbD+g7UZIokRO0HHjGM8wGOzfiSiJXPOq13Iu+wT1tWUSzSb1doelWotQVpF7S7zn13+LV1x//WWzR3xZJ3zY/tX+zX/3W7zjl36Rp594gm98/etMfuMh+hWN4UIXuXQKXVHxNY1MOkFu5y5KssGT93yBwXQaxQ/RCwafW7mAuD/FW35qN30DBUQJZqc3yDY6XNtTQLYDOp0mkwsb7C0maUkCuwspmm6AH0d4IciiwN58gnM1i71Zk6GEyhPlBdZXh+nOFejO5Bjv7ufzL7xAFEWX/BE5l8thtxoAbG1u0mjWGc33YGoysiSiyCK2G7AVBwznDDIZnXMbTTzHZmN+jbGJfgRJxO84GAkDq+3iBgGeF1AqZehKqAjtNt2mwly9Tv/uQT737AKH9/XTm9B4/Fvn2T8xxuNzDYq7hukfmuDf/O6beeAzn+BQ2OHMiecoaBp5WcAJt1+2235AzfGpugEjgyOs+QLjSZ20InGmYrEjZb5ofiGjSDKCLH5HXO1Sks1m6d+zk3P3fpM3ZYfJSCorYQfL90gio8YioiCwO5liubWBbwUEgoKIQCKQoObSMBXOSE32jvQQENMmoDeRpO21IZLJazrtrSa+LNFodCjkE6iijCAIRFHEhcllUqaKkdBYmt/C63gcHdjJ0oVZ0t095ArfP25ZlnnHO97BXXfdxdTUFBsbGxiGwa5duy7LLvHlzBWHDrGpCkxfXGSjqnHrcJGCnsAOPNohVBwPWZLoT5k8UKuwX8pxrtPg2GiekXQC5UX5lZl2i5iIffkU3brGvTNlLjYsRtMJEESIRJZbbcpeQHehlxtvvBlJkjj90P3su+4GItdmfWmRMAi4SpRwzQzDV13H4aOX13PjZZ/wv02xWOS1b3gDr33DGyiXyzx8/wNcPHECN4yIEiYDe/fy+lfewMDAAJOTkzz18FdpCAGFhM4Zp0U0oHHw6mE2Nuoszqzj2T5Tzy9wVT7F6nqNUtJgplzFdTpcXPfRZZFIEkibKi03QNEUJEFAwEMSoOmF9JoqvmfRaTVZ2lilO5NDlrcNsb89LHYpOX78OP/nLz9JbXOLRqOKLYCpiRiajCwJKLJEx3Zpl9d5w3gJXZI5sdHg9t29lNsNmvNr6KU8tqrghDGGqVFdr9Ja3MAIQ5amV8n3pFjp2AznExilNIMIdKo2rhDjo9KKDcyBEW5/3V2Mjo5hvFiK+epffQplrcJ9c/OMqBIFVeZ8o00nCFAVAVHSCEUVL3RJKhLzLZuG6zFRSDBvOYSqhiTA6PgOksnkJV87RVG46hU3MvPQA7iOja2LSP72b03ItruShIAtRgxmDJrtiBY+WURURDqrDaSCjpJPI9gyy56NG0eEEeRildpcFXHZwe5yWdqs8w1BRHr1FeSzCSzLobxapSubYGxXP7bjc+LpC3jNkKPjO7CrTVYaPj9169G/NwZBENizZw979uy55OvzzwHLsvjv/+n3uEpNkR/ey9eXzvGCYWE5Eboi0gkCIgTiSGCt7XBVT57nVptYkcfNchd116MV+qzaHTwhYF82TcsL6U3o7OpK8tBihSgWieKYRqfNhaZLX08PR44eo7+/H0EQ0G57HdNnJtlanMPoHSGKIrRMnj37DvydHWeXkn82Cf//plQq8dZ/8Xact9yxbaIhy9/V3jQ8PEymWKJXVugx0jxy4Rk23San7z7FkKGRiiFoOwjrDVpBTDab5OxcmeeXtzhUSFNIavi2QM1yiGKIBYGUqSGqCsQxCVmk7QUEUYTje4h+yPLmBlfu3Eu5XiVVzF+WskQymeTma6/lG/fei9mdpWu0n7mNCvuHe5EEEaKI6bOzlOIO493dPHR+nUCIuWGgi5ogMLmwCoFPxQnohFDfalJwoUfT8SsthjoeVnkNJ3DZ2tWFH0Flo8nWVoPBkWF+89d+DcsXOXD9bXR3/03XyMjICO/6jX/N1NTr+PJffJwXHn0Ye2WZHkNjLGMymk6wtdXhqZV1DvR0c6FuMde06U5ozDVtLEVjtK+XZ6fO8Ts/985Lvm7fZtfuPew6uIfz0wvkOxYDYhJFELHwcQlxCPHjiIKq4hIiABt0EGOIai6zZ5aQsjJDYY4YMFGouTZb5QblxTJZU0duCRzMZnn4kbOkNJkdO3voKqQ4uHcA3dSoNzo88cR5zp+c57YDV4EscWZqkX033fqyluX+UQjDkMnJSRYW5oiiCBDp6emhWCwyOjr6Az8pf/rjf452YZa3Hj3OyalzHAk3ubZUZM1qU/VCvFig4ruYkkLe0BlPpahmfJ6pdHi+1SBugiDGdGkaO1MpYmI2Ox5FXWEsbfIMNRRJxBAlViybRLFIK53gja9/83eG1fL5PNdcfwOuew2O4yDLMolE4jKu3nfzzzLhfxtd179vW1M6ncbo7qE8N0vohDw7P8uhvMm/uHYXRhjTqrbBjxiRZdJJhQfnNsmoEtf35ek3VUYKaS6W62iSSFqVWWzadLyAtK6i6Sp+HKGFIs9XWkihROA5uL4HwFR5hWve+abLFvO73/VuTr3/vZx65BEOHt2PI8oEFxdIiRJ2ZQu21pkomTy/Xmeq0eJwX4aMoRAFMf2tDo+eOE+6lEVrO/TUAvoVE83tkIggQCLf3Y2Y1JiPbHYcGCZ1bYIHHplkabFGve3Tt/eq70r230ZRFA4cOMD4736QyVOn+Nj/+BBLJ5+kutXk4bUKROCpNk9HoKk6nTCiFUEil6FYyHNxeYPbbnvVZdUK13Wd8Z17MNNFzj73PJv1MmoUU4kcZmlSFHUSKGxGPhYhQizQIcCKfdbiDs0tD/eZacKWTSJh4MYBccsnEQmMmhlsAhp4uI2AaKHNs/efwaq32bV3iEqtQ71mceHMImuzG+zNDyPHKueXGhiFEvv2Hbhscf848H2fMAxRVRVRFAnDcFsW5W8l7+eff56/+tjHEBsNnEaTrfI6ncBGScuksmnkVDc3vubNvPmOO/7eCeCtrS0uPP4kr+kZRpFlXNsmp6nkVI3+rgSaJBKEEWdbdbwgxBUCkqJC0dRI1GReWSiRUrfr6p0woB0F2JHPSsemS5dZqNtsWT5/PblOrICeTDLcneL617/x++7cNU1D07RLuqY/CP+sE/7fhSAIXHPrq7nnv/4BL1QXKYQRP3tkjGLSYHluk6jjkddUiqZOd0JhUFeIiLmylGWu0UaKty385hoddueTlEyVqu2SShkIgsim49Ny4Vylzd5UDxtti56+Pk7NXaDTn+XqY9f8wwf5QyLLMnf+zNuoffB9NE4/ynKkMhWEFASfQ30JYhNWwoBKJ2S4kCSrSuiKRNBoU6t36HMdNqaqaLFKr5TmFcMpetMGz681yHTnMAyVVsfFtlwe+OKzvP7tx7numglOqPN86msP8ydvuuvvPT7TNLn6+HH2HDzI5//qUzz1jfsQ52YRXJtm0+L83CS+niPfO8JY/wC6LBE4Ptdfd5R3/sq7LutYf7FYpCXIDJaKXHPwSlYXlwlWqySDBCutKma8XbM/32whxzI+EXEcM08LgBQKuapE0ZXQ4whJUJBUjViENCp6KFB3bBpVmx12muB0yMz5s1zonSZjmsihQNgJSCayHN1xhHbgc82ha1lVPfL5/GWL+1LheR5Li4tUqlusLK/Q2Nwg8gMcxyWMBcIwpO12iJCR9TT5QhcrK0s0ahUgRpElBge7OXbsOEeOHOHsmTP8xR9+iMMDJeaW24SVLY715xjsH8cKPBb9Fl3FgG999o956Bv38dv/4YPfd7MBcPH8BQzLprc/T6dt4zQt0kmdVhyQjGWiKEYQBNKyzGYYYsoKClBxPfrlBJ0gJPtih5MpyUTEiEKM5YY4dsxWM2BYSlKITCwnZCmMqWW6eMvb7nzpTsAPwE9kwgc4du21PHr0ah7+0z/l5rEsKVnCanWo1trsTG97lIZhxEy9yVBKp+4FZDSJKIZKx6U3bXB+o8HJjQb9SZ2YmK1mh2XLYbpiMbVukQ1NUorK+VaNwG2h9+/iXe9/D4ZhXNbYent7UTWVIyMm680AS04RiRKbUowRWxzb103gx9z96Cy7MzqGIrNpe0hhSLlpc6gng9KRGVGTFE0NywnwRAlZkUikDAaGiuyTBOqnp7C9EKfjc/NNh/jv//vr/O+P/yVvfu2r/kGRuVQqxV0//0vc9KrbOPvcSTZWlwGIJA27YhF1bOJYQEnoHDp+jAOHr7jsj766rjNx5Gqm7/syvYikEiau1iYhKay0KnTw2fBs7HZEv6CxGdssxRY9gsFRsYtybFN3fYRlnz07SwRyTBjH+EKEbfuck9ooPUmSpkFvl4TjeBirNbwlm5Qg0JXL4yZkuoaHUVMm+w7vZ8mqsOcSC2hdDi6cP8/s7FlULWbp/Bmc6iZnTl9gY3YdpWWjE1F1PDbkFKGeRjUTeJLM8K6D7Ny1i2xKZWJnP8vLy9z9hS9y8uRTzJyYxC9v8IXTzyE6LsO5FE5Zpew3SBcTjGe6aMk+tx6f4M+//DTv+dWf54433cnNt7yK3t7e7zo+13VQBBFRFHFdmxQCrVCiFfgYkkhR0gnjCFXYtuNMSBIXOhb1tscBPcf5SosuTUeVBEBAEyTm2x26NYNy22em0iEZKXiBgBgr7OsexFByTJ56jmPXH/+xnJPvx09swhcEgTe99a08+9Uvk1ddGi0bP4zQEciZGsRQTBo8t14hoQiUDIWmG7C/mOHhlS12yCJ9xTRrlsuMG2K1O1yc3eT0Vgs3ENkbZelK6cx4HcSxQd77Xz/I7t27X5Ibd2hoiBdmymwJA/Qf2Mm+vYNk0jpCGDE5Oc9/eeI8v3pFnt68waLlYjge8x2f4WyaZMsjpci4vkB/RqPaaLPphRjpJH29OZIpE0GAIIoZTKXwXZ/rrtvNqdMLHDowxJmz58hku3itYfyD3TSSJDE0NPQ9Q1RhGNJqtYjj+DtSFS8V191wI2efO82Z508xgIKoKgSBgyEoBC6slF0UV6QheCRimV1kkAQJmxA/jpmmwdpih4t+g0ODPSSSGjXPpWzGDPf3slJt0oWKb0T0mwlGu0qcXJxn9eIWKb2I2d/FxLEjdPd0s2BVGDi6j/HLWMa6FJyfmmJj/SJXHtnF/V/4LEanyrkz5+n1fPaPllhaKlOpNXCNPLvzPSRUibO1GsWeHYhtiwvnzqEbOs+efJIDe4fJpNPcf/+DXHzuBMf3j3HjrbtIGxqW5bC1XKexVWfEiXAq8Fy7zMThfm67ZT8X15pUVp7iLz5+lqGxg7zpjXe8mOBdzGQSKwpY29igvrmJLgh0AgkZiUrgUfc8MopCxXexQh/fCZls1ugnQbdmcLpa5R5/lX2FFKPJJH4csWHb6KLAF+fXKQoGxCqyrLCj0E+YTGEIJs8/+iSHrrrykk/M/rD8xCb8b7N37178CyfxOwHNjktWFIhjEAUBCehLJ9hqWTR9H12USBgxwz1Zlv0YkZj+vi5ars/cckRzR5HiYEDz5DJXZ7p40qmzmZT5xCc/QV9f30sW0+OPP05U6qb/uiOUcgmaToiqhSR1lSNHd/GsqvP7Dz7FbQUZRJGWG5JJZ9BMhawuY/khpqSR0hU6bZeNus2ekW40Xf1OzzjEBHGE3/EpFXP09DU5d36VdmuTTHGQ0y+c5dabf7j2SUmSfmx946qq8gu/+n4+8Wcf4b7PfwGtUifvq0xtWnQ3dEpxEoQYVZDwiegIAYtRi3reIOztQciMYUfw7OYyT01NMSCI5NMprty3C3E1wI98mnFIUTSJRIgVkcEdfVgpjdKOA9gZFa9oEo4UuHr/K75np/pPDcdxmJs7x003Xsk9X7gbtlZo1BrsFATGe3OoApR0mY8+0eLI6ABXdOXo+A71WMbUfAJrEbEtIOgqniwQ20km9k1QqwzRl3PpS2sMDGbIJkxUZPxd/UyeWWZmcgM2ArJjCfbvG8T1QubX6yTlgP6swulTD3HyqSc4OrGbQjrFxfl5Ts2cp2ejyr5cF54Y0yfpzNUaDKZ1krJMw/c506pjCjJ+LLK4aRPI4Coh46UErSDgyY0KX1pcJYpifD8mGSsETYHN0KdPMIhlkUqnTUqXETsO0UadjY0NXNflueeewzAMjhw58pLmg/+bn+iEb5om1UaTRt0GL8L3AqwoxrF9REnY3mEisuAFVDs+3ZLKshixY7SPvaUsqq7hBiF+GNI2TXJH9hCHEX8+82Ue39pio6Tzmrff+ZKf3Ecef5yhI/sY3bODzbUyluXSsj10VUYUBMSkgdPTyz1nzzJsapxd67B/YgdbvgeyhCaLzLZb3BgXyesKMjZhFG9f5EFIFEe0XZ9a4LGje7u2PNDfRXmzTqm4C1XXWF1Zot1uv6QdCJcKwzD4lfe/j+M33ciHP/gHTD32ArqXICUpDAhJqoGNiUwoxFRDl/JQAXl0GMtvINJEMFSMHYPE4yPMXJwhYchosYQVeThxhCkouEQUzRSpTJpa1GRksMCOa4/zujveiKqq/+RLON9maXGR3p4slUqV+sVz9OsyuC4pTcWQRCCm3HEo5vNc29dFRMzpchNVgF2mTranF83QcOKYSBKob6zyyMoy4/t2EKQHmRjQaG22uHJXL7WOS60WsHtPH9WtFu5aG9cOmZ1dZ29/gdGUytrCKs/R0ZaDAAAgAElEQVQ8dArJcdEkia3QQuobRJ45z+0Hhnny2RlkBBKCgGkLXNFVYqZRZ9quse5bLHTapCMNyd02UzrUn6VkarhE5BSFlJ6mN/Q5t9WkXndAEqjYHnvJUxFd4hiKboqtjQrksqy4Fd79nl8i1kN6hkpEUciffOLDDJXG+Hf/5gMvub/GT2TCr9Vq2LZNHMeU1ypo7ZCtrQ5qYruHtm7ZpLXtN/I5RWGubqMgossKid48+XwaWZKQRQFVV7mwvMVGGNOvKOSTOt17ejj51DS/+Ivv5LbbLo9uzt9FHMes1LbIdBcpdefI5hJUNhuUy1W2am1cLwBBoDg2yMzCCquVOrf81CuJij2Ul5YhC1nF5ozY5vGNKlcUsvQkNVotmziKIN7+IZyvNVByJn2jJWIBWu0OtUaH40d3IgCyomPb9ssy4X+b/fv388f/30f48498jLv/6CNU58ok0JBliUrg4sYBS3mZZH8vilRnbKhIvquIkU5vm2pUGzwrQifhMS2C0XDJopGUVRLJJEomyQYuYU+Gm191M8PFiR9L58aPQqtVp1TMMn12koIqkJIiFpsOA8b2ebeDkLZjM5hNE8Yxqiiy2qxxdGwUM5FCUiSShoLkh7T9kGLCRPEswk4LXdPp7kphNx0sy2WgkEKXZVbLLXK9aZabNo7ncWC4wLnpVe55cIr9hQyvSCbYMzZExbK5uHCRtfIyVschkCTSfRrfqsyREBUiO6ZYN0grGqeaWyw5NqYg4/jbXrPHhgtkJI0UGqYYERLRiUIMWcTsznNGqGOGEikTNtsWNBWiQOWiVWEsVWR6eppnKHPkV27i5jfdiG5slybLq5s8/dAp/tVvvZf/9v9+mLGxsZfsfP1EJfy1tTUmnz6Ju1lFFyQsx8ZqdpADhWfXW4ykZHIJhfUIJESiKGJys0mlGWzrjBsWV5kDROF2KcNq2ixVmjxbsejaM4qmyLTFiIGRHlZqAWPje/9RIl+XAs/z0EwTMZVkfaPG+GgP2WyCHbv6t3f3gkAQRljNNssnnqeQTPKmn7mDIDQhiFg59RRbLzzKLYdGOH2hQlhrIkQimmWzuFYlm08w32jhJmQyvQX6+ws0WjZPn7iAkcwjygZmMkVlLfgn62v6j0FRFH7xl99F2kzw0f/8IZ5c2WKfnEd2JTw/wOpK0pewGZwYQ9I0jGweSRTRFZ2smSJ0HNbcCk4hjx8FDLZiNEUjk0pjSRH1tMzb3vVzWI32y1KcTBQlbMchaLcgjknJIkEUANuDhV4QoYkxmhBsi7W5Dl0amIpKGMWYqowkiigSWM0OSSVkuJRnul6jo8ZU6gKhEPPcxRUOy4N0goBW6CIkZNbabdJ5jfsfOce5M8sczaU42J2h3fGo2za9SYOg7XOhXGXZbTPSZfL2w4PEUcSZ1S0alsOZ9SZfmq2yQ8+zP9GF6MZs4bBBi1CKaAQepiwhRwJtcVtWPQRyhsyOgsnqls0vjg5T8Xw+t7hKpdIm6yfYdNsst+v03TbObT9z83fN3ZT6irz6Z27mfuUh/tPvf5A/+9OPvmRPdC//O/IHZH5+nsfuvge/UmNlYYmtzU1aroPXsMAVqYYR8/U6CS1mNGnwRFxhy/YQApGjmS4qtsfdF9a4oCscH+nDjkPafkxH1Thy9AC7xgfQDYNAAhkBMRA4ds0Nl00T4+9CURRUSaJ/ZIy5+bOkUib5XAqB+DsTo34QcG5qgfJajXe+930kU4PsP3AFcRzzaBQzu7SEtXyW7i4Dqx2zWbeIqg7N2hbF3hzF3hyRrnJgfz9nzi+ztLLF+ektrrz6lSQyPfieh6mJl9Sp58eJKIq87qffzMz5aZ757NdYbXrEQsBsVCUwNfI9OQRZQU4kvpO0gxfXunewj8WTizg9IlLGZMHdomSodJIBpd1jvPONt5PL5njy/El2XXXFjzPMH4pCV4mL089i6hptVSGIPPI5g2rDocs0kCQRRZLIxA6W69DxfXbnk7TaDcx8EVHYfqoOiQlDl3w6gSyJNDYrNBUPazwBmsx6tck9J6foLSQZGcix2YRlt4Y872EGWWQ3pOl4PDi1yHg2SW9CZ7HeIAxDZmt1jo5k2VFMEVgdHD9gSFPJ5bNc3VdkJJvizFwbAZEw9sELGM8lWPXb1AWH1diipOlkZAVREkkpEm4U0sAjkmJiQaBLU7mtr8SXvDJiO2KqWaacjnjzG4993yFLRZG46vpDfOrU3UxNTb1k09E/EQnftm0++T/+lPWnn2dhcYGm1UKNJQqagSmAGEC3oAMSq5UGc5UaR5N5ditdaLKAa0EUCIwLKSrohEPDOJHLeCFPX08BQ9cQRQnf81ltWZyfWuG6Yzf8WHRMRFHkqn0HmLVtBocmODs1TamrSbGYRVMkWm2bldUKzz95gXfd9W7e8XO//F1lhJte/Rp27N3P3Z/+JI8/+FWEdp0wDHFaLsVSjkW7w2ZbYqgvydkLq/hehBuqZLp2EIkpBoZ3sbJwgeOHdr1s6tA/CKZp8uu//Vv8ScLk6b++l3QrIieFyCmNdDq7Lb6mvdhuK4CISESMpm+XcJrNJqVclu5Cjt5CnuOvu5WhoSFkWWZlaRVDTb4seu3/Nn19fZw+/TRxFGKk81TKC4x1Z3hybYmkpuKH4Poxg3LASnOdUE5T1DVst03LMtAMDVGVaHVsFDVEliVmVjcQtYA7bjvIwYl+Tp1bJmHKDJTSzCyUeWFmna2qhS7GJDSRE1Pr6LKIG7j0pnUmK1UenF9jJJPG8KA7rXG8r4uy70AUMWTqdNwIzw3JSDI3DhU5t1pjubXBeNZkWDYJjYhdpSTLbZucoqKIEb7kk1BkBAW6VYUuPc/XmhssdNoMmiZFQyVpiHRcjwudOqWdYwzuHPw7167Y00VXf55nnnnm5ZHwBUH4L8DrAQ+YAX4hjuP6i599APglIAR+LY7j+37EY/2hiOOYj3/kozxy91dQmzbpWORqvZekILMV2EzZNTqxwGoUU1Q0skaBmXYdxxZpxxFCJFELPCwvZJwszyzWseKAq/btRJek7RbF0Ge93qBu25yaWmBtfpO9+0Icx/mxtGPddP0NnPizP2HPLTfQ29vH9IUpJs+sEkYRsqyQUNNcMbCTX373u7+nZiwIAkNDQ/yrf/sBrF99P88//zyr8zNUtiosrS+QycgkNAE3cHB8HTeU2NjwGBzbQ9/IBCtzU+weKbJjx/hLHvflxjAMfu3f/iYnbrqeez79OeYe+haSb+MKEQoRhrTtoRwT4xMDMQIC+YTJhbUOI10ldo2O0Fsq0NPTA8Dc7DzrC1Ve+Yqf+rHG9sMiSRJXX/0KPvfpj7K3O8va6hI9pkrvQJpzi1VwoV1t42ckDid9Pru0xJqmsrMrx2JtlUrHIpUyicWAgYEsi6sb1Owao2MFerrTVBsWoetxaFc3YQw3H9vBVx+Z4p77n6cQRVwzkGVsXx+9GYNK2+OppRpxFHPNUJbHZys8vtrilw6PIABtN6Coa5iihCNGxEEEUUBKihnIaOwuZOlREiRFmYebmxQNlVJCwfZhy/a2Jbt1GV2S8KOIC80OB9IZyrZLj6HjChEZQ8LuhIiSQKI7hW7+Pfe/ALIqY9v2S3a+ftQd/v3AB140K/994APAbwmCsBd4G7AP6AMeEARhVxzHl9ag8R8giiK+cf8DfPwPPkSv5TMgmIhA1WnTESTSksKVaoFT9hY1x6bjewRCiAzMuy2ECBA1FEFmQkmx9v+z995Rll31ne/n5HNuvpVz7Krq7upWtzpIakkoEpRACDDR2GAwi7E9zwHb42d7xmsMHvsx9vg9HHgP7PHYwuQkIQEKCKWWutWtzrlSVw4355P3+6MEI9tgEFKrlT5r1aq6+9Y6Z599zv3evX/7F/wGlcUS37xrL8IJ2DbSw0KpxFqtRjIVo1prUClWGehrZWbyJPfe9U3e/q53v5iXDKzPuj5w+zv4x7u+RnK4j/FNW7CiUSrFMnNnz9GYX+GjH/zwTzS5xGIxrrzySrjySmB9pTQzM83k1BlWlpfIZPNUGxIDgxuJx6NEwhKX79lKb2/vK2p2/2xUVeWKPXu47PLLmZqa4k9/7zfQZIjGYiiqgizLyIqynkhPkmgUC5iaQSzdQnHZ4WxxkmxPHjmZxq65dLb3cP01r39Zb263t7dz/Rvfyn1fvRO3onBu4jw9umC2kCe3WsJrhEyWVJriBsmIzlSpSNwMSWgmpfoaubrAlhWqtQyyFXDpzkEyXsjhY3MYhsrGvjTtzVFWcjVWczVW1iooDZvLepJYikJT1ESTZToTJrdv7uChyQzLZZurh5qZLzXw1ZCpYgVDl9AkcMKQMBQYikxa11h2GvTGTJpDA4FA6CGj8QhzlQZXdqUJhCDvuZQ8j4SlrifRCwWz5RrXJJrZ5xapez7NmoEuFAQBmiJTCR10/cebdF3HI7OQZez6Fz5V+o/jeQm+EOL+Z73cB/zAJeV24ItCCAeYkSRpErgMePL5nO859o2nnniSb3/2n1CKNbaa7cy4ZXq1KP16jKisIRA0woCrox2cc0ssu3VqwqcquZR9j3NOgaik0axGMGQJSQnpiOoUpwvc++VHuas9wvbdI4xs6CZTrOCW6uwY7GL4+m7OrhR5/Pv3svXSSxkdffFu6A/Ytm0bv9fZyWNP7OXAdx/G8T2ihsWebZdy9R3v+Zns65ZlsXnzOJs3j/+wLQgCHMdBURQMw6Ber5PP51EUhUQi8bLciPxpkGWZ4eFhrnjd9Rx+/AF03USPRlH16HptWSFolIpU19YohzqaLDPeN8gGM0J62zA7tuwiHo+/KCtAIcQF/wLetGkT5vs+zKGH7uN8oLF//176DdBiOmcVD627CVdWKIUhEbkFXzdpSkYpZUMiMZ1LhjrYMNDOQ8fP4asaA71JyhWH83NZQtthZbXM/GqJbKZMtVClK7FeZSplahxbLlJxfK7qb2JzR4JbNrZzz+kVRtviNEU1So5HmAwJQhkvCKn7AaqsYMkybhgghMD1BFFTJUCAJNjRnOTuhVWmo3W2NMXptHSWGw06YwYl3+P4apkOXceTQ1RlfYu6Fvhkqx61wCMSiVMp1MksZ2nv+dHxKGePT2CXfK666sWLxH0hbfi/BHzpmb+7Wf8C+AELz7S9aCwvL7N0+CTTJ0/Tp1rreW4FdKoRmlQTDQlPhCiKTLNqklB0VtQaZ5wSU8Lhiu4UG5IWazWXbdE0tSAkpep0WAb3La9yKq2wYVMfV28fQ9NVBrta8QlZml9jJZNlx4ZeqqHEQw89wMjIxbFnt7W18fa33sHb33rHBcm/D+tL+kgkQj6f56mDB1jJrxCJWfiej+RLjA2PMTIy8ooUflmWuf6WN1NenmPy7ElKq8vEOrrQNIOgXkORZLxAYq3u0d+cJhUEKK1x9rzuqgua+TKbzXLgyX1MHD1ObnEZLWLRMzzI7mtfx+jo6AVzJBgcGqK55X3MTE7QMTbOt+/6BvOrU2zZPEDKMmhSFSaLNp39G6jUqpysl/E1nQ0daSzL4MjUKpPZBmkXmoKAjd0J+los5uYzFFcLpIKAgu1yTV8TZ+oNLm+J0BTVCUMo1F32ns8BsKktTtzUKDV83jDSzgMTa2xqi+GHIW4QEoYSmgyKJFHyPBZKNpmKgxKJEyKQJLBklZ2pFKfWKhRqLoEkmK85lOtZLGRGIzEsTUEVMrYfEmqCA4UiFTdg3nEY27Gdecvh7s/dx9s+eBvN7f9yP292cp7vfuH7vO9tH3hRHTt+ouBLkvQg0PEj3voDIcRdz/zPHwA+8M/PtQOSJH0E+AjwgrowTp48RaNYRrY9DCGz7NZwRECzahIKQYMQJwxIKzquCAkRWPJ6cqw11+YKI4muqAzENAxZpc3UaLUMTubL1DqT3Lp7mKErRmnuaEKTZNatt5CORzl0dBJzZY3O1hhTmTWKxeJFL0RxIQX39OnTfOYfPkO1WERyHBKJJN0bhxnftY1zS5NkchmuvOLKf9OHQqHA7NQUtXIZTdfpGhh42dVO3TAyyuvf9YuY936dM4eeolgsosUSYFpkijVm8nW6060MRiK0D/Vw/e03XxCxD4KAubk5HrnvQSaeOohWrpNSDDpCgRZWWTs7zxcfeZKOHVt470c+fMGex0QiwbYdO9m6/VI8X8XOzNPZHiM7O4lTrTAU6WLzwCCO71JpODxy8ijnA43Vos/g4ACXNLVhmQLT1FE1mZisoWs6Q00akwsFRpujJAyVCAJTVZCAmKHQZEWwdJnvnFlloClKTFcp1F26kxaFhsPR5TLb2hI0mTr5hkfDD6gHHtm6w9PLBQYsk5znMl2usykZxRYmLRGNlK2yPZbgydUCohqSabhsSkeIajJuKFhxGpQcj/2NPKeyNWRPIxJJMbBlI7/1Hz7Ix//sj/m7P/sco9uG6B/pJQgCJo5PM3Fkhne/5X28773vuyD34cfxEwVfCPH6f+99SZI+ANwG3Cj+d1WPReDZ29M9z7T9qON/BvgMwK5du16wqiDZ+SXqtRpqCLXAox74NGk6iiQRkzVcEWAoCj7gihA79KmGLhFZJSUM6l5Ii6WTrboEAhK6hh8KTlaq7Ll+F3N4xFNxWhQTU1YIhECVJRKKTn2wm6eOTDA20o/j1KnX6xdd8C8E2WyWT/3tp/ni1+6iGY0u3aLZNHHIc/7oBIe+9m1iYz0Mb9mCIins3r2bWq2G7/s8tXcvBx54gPL0NIHnYVgWrSMjDF96KdfecsvLZrwkSWL7jp109/Zx+vgR9j70IBMTk+RXi+iJFHfceDlbNm9m687t9PT0XJDYhJWVFfbd/zDzJ89Sn5nHKpRwyhUKQcCSUyVbLeMGHrqsMTs9w8LsPH/0Pz55Qc1JS0tLOJkCO8bHMA2NwUiEJ449jSklaAQhumkS2g26O9LE0wlcX0YxI0QVk7nlGXo7WgCZesOjIx2lUquylq+zrSvByYlVBiMaIeumFHm9JBpdCZOOmMFMvkbJ9onpCvm6y9bWJIv5Bsslm6B//bN8vlJDCDiXqWJ4cP1gKz4BPVGLM8UqE5UafRGLIISC66FJMrvTTWgyHC+UOVfMEFVlpkoNChWfuhcQCSzUUMfqb2f7NXsYGxvjn/7+Th5++GG+8JXP8/DT+1FUhe2bL+UT//DnPzaz54Xk+Xrp3AT8LnCtEKL+rLfuBj4vSdL/YH3TdgR46vmc6zkjACEIRIBHiC4rmKjrwi/rOIAMOCKgEngEAkb0JEtegy1GE3m3ji5JIAtc4SMhkbNdvHSE7o4mTs4ukJQ1IrKKQCADEhKGrLChtZXj2hzVhkutXHxFBCD9a6anp/nEpz7NselFxjo3sK1/A7oskctkaOTWMEObMS0kO7XA8fwqTz/wfXZfsodNA4M88Mj3WTiwj42myaZolIiqULcbnH/kYb6zbx/Li4vc8q53Ua/VCHyfWCJBd3f3ix7T8FxobW2l9YY3cM0Nb3hRz5vJZNh79/10a3HOLK+xtLTI6tIidq3OWqNMQtLo1Cw8Qsq+zVqlwvzcHP8nAX/5t39zwfq1srBETNFRZBlJWvdXiuoRGt6699Lc4iLFtVUEdRJpg3oQMjt1DjOapJBvcOjYebzRduoND9t2OTmxTDpm8o0TSxw/MsM7h1toiWo4PsR0FQFIAvpSEaZzNVYqDa4ZbOHgYpGx1jiVWsCB+QJfPrKIroKqyrRFLUaiMWIxidOlMmlDZzAWwVBlvj23yoJRJ65oHFwrslx16WuPoCATFTq5gs2RcoWIa5CUTOTQx5MEU7rNu995O9fe9AYkSUJRFG688UZuvPHGCzbWz4Xnq0R/DRjAA8/YqPcJIT4qhDgpSdKXgVOsm3p+9cX20El3tlFdXKHQqDOsRVh1aqiyzLJXw5JkdEXFEQFCQCgETbLBjFtFFzLDeppvlYvM1uqkDY2641P1PWwvRNLWEyU1KTqZtSKtg7H1h02SQAiQJHRVxVAUytkypUL5gqdDfrEpFAp87q5vk8EiLals6uwgZhqUKzW0SBRJ7aKRW6bgO2xImZwqZQglj8fu+waNnddx5uGHeGt7G0PxGIaiIIQgkGV6NY2jxSJ3f+bTlFbP85bXX4+uyGRmHE49FTJ8yU7GNr5Wvu/ZHH3yAL1Gkn2HnsJbWCVSrDAYalQkjSsSfax6DVa8Bj1GhD5NZtltsNio8f1/+hIfKVX4/T/54wtSWi8MAqJmhFK5xmBfBwurLiN9A5zYdwi7ViPiVjHkgLok0d/bimpEmJld5OjZs6yUy0zOBxw4Oc/wQCvJiI5mqCxlimzsTHLFe/Zw4NgcJ44v8XNj7ViqQkRXUSUQEhxeKtEZNzm5VgEBXUmLA+UCUVMlEqqcyhW5YrCNVkunPWqyVKhTsT0QAicIyXourTEDT4SshQ6HCmWivsoDizmW6jZuHUJHYkBJ0a8mqAqX42GRZc1h9Obr+civ/9qLmuH1ufB8vXR+bN5WIcSfAH/yfI7/fNiwdZzSzCIN4TMUaWPFrWGHPglFZ96r4XjrZpxzoc1axMRVasQI6bJddmjNDAZJ9q8WuKwtxXg6wZlCicCWkXxYLtcYTTUzNblMT2cLCdP44fJSCEHN8VCROXN2Ds1sviD1ay8mJ8+cZbFsQyhIBevBRWulMvUwQNFUJD2G5zSTrxWQ3RBDTeCoZW7YlOQre+9iPBWhLxolrigYskJEU/GE4KHlFQ47JUa7IqyeeJLHZZvh4Q1Y8TS65/O9r32e/HU3sedF9Gp4KVMsFqksruHbIUq2SFpIuG5ItlFnQI8igF4jSo8RZdauYKkKg2aMtKoz51U58O37+awR4R2/8mEu3fXv18t9rqRamshaFiura4wM9iI0nbRloeGTmZ+ntcli25ZezizlWMmUyVdWcKWAa2/YTkdXAqHK+J7LoSPTnD55nqH2KNs3daIGIQlL4xfecilf+PYxvnUuwzvGO9AbHk4Qcs/pFSZzFSxVRnUV7rikl73nc/iyxIa2GHYQ0CVFGRvsICbLrBbKOLpMmxXlyFKBmCWzsTmG4SvMFOvIQuKathZcF2ayNnJdodk1Sak6q2GDRlgCSWLRcNiwcyv/8bd/6yUr9vAKjrTt7u5mZmwQKxYlCEL69Bgl3+WEXWC72cSqV+GxJgurbxODqU5URaPk1Mjm1rhn7jy7gybuK5U5pdfotiw2t0bZt1JgOVenNrXAJTt3UlrJ8tTBs+zYsYGoaa6nDpZgemWN8xMrnHhighvev+0VNcMPw5Az03M0AjBUGU1I5KtVjHiEVMTEMsz1IJeoQf58jZpbJxJLsriaYXPMYDAd4Yq+XqbmSuwUCpoE0+UKX2sUCNpjXDW0mb6eZoq1BtPzs9zz0COYQtCXimJFYzx5/7d56No38aFf+T9+GLz0aqVaXV+RrszP0mrFyLiLzJdyxISMLIEvBN16hDm7goegV4+QUk3qgU/UVnHtAqcPHOTxuzpp7eigp6fnBetb/8AAx419tErtHD4xwcaRXhbnZ2iLxTh1fpr0yBhxU6c9YrL3yCR9o+3s3LGBwf5mTEOh2HCwrARNqShD/W3MTC5g+wGpiMFq2WaoM8mb9mzg09MZHpvKEjc1Cg2XI4tFrhtqpjMRQdcUHp3JsVYPaUkkSSejfPfUAqpl4WkKibhFwfOItSWYXsnjWjKtSYuaH2KEMi2aRbdpUXZdQk0wU6/jZ2UGjDhIYIsQU9eZCSsMbNnIG95yGyMjIy/YGF4IXrGCL8syV153DcPbt1I4fB5ZVpAkibSic39liYW+Tno27iRUVEJVpRoGtEZSmLEm1pJN7Dv6FNf4Pdyzep6i63JTdztbm+Lk6i5PH5riRGcnr+vtZ2oty333HqS1t5lEMsJqvsID9x+kf03DCBW2XnrZS/ob/7ni+z6BEAhJoBoqZdcmBZhRC1XVkGQQIRimSSRqUaxnmDwxhSoCjuRsfM/me8VZdvS2slioI4chn63m2Hr1OG6lRldXE5GYhStJnJ9b5bbhFra0xUlGDHxfEGgWJ1dP8cnf/XV+67/9xQsqUi83ZFkmXy6SkFQCIZjNrOL6Hk1GAjcM6dVjLDl1+sw47VoEXxKkVYNm1SCpaWiyxJNrOWZPnOHc0WMv6FgahsHmK3cz8fBTmFKKg0cmiSc1Ts3O0JzSaDZ8FubmKddc2pvjiDDE0GUc18UyLep1h3yxythwKz0dCdIxlX17T5OMGWiGxsJqma1DrQwPtKCUKvSkTCayNa4eaqE5avDwdAbbDzENk6GONupahMOLDZq27sLOraKmExDV0RwXWYOh0U4u3dBKPVMjt1ql2PAx0aiEIWU3pOEEjKhJKtEyIRJVPDwk3LhMmGrhF9/9bmKbRonFYi/YGF4IXrGCD+tRkTe/+x0crn+F1OwaxcwyVRFQj5q0DWzEV2RcERAL1PUCyoATBiQSLeS6enCmF7nJGuLu7Ax3NubZkIxiqQoiX+ar9z5BZXeFzUM9jEWaWVuq8uS+SeanV7nJ7GXOy6H1tnPNdddf7GF4wRBCYNs2IvDRZAVbUagaICsSjZpNPKX9IEkige+yvLyIXiww6MsMdffRE9XxSjlcITEzV+bRchYrqnHlrZeze+cIxcUVto11U67U+fzdT/KmgWbeMN5DoVjD93wicYNsvszu/n6aSw6f+cv/iz/+i7+6uINyEWlqaqIqXOJCsJbLkK+W6dAsIrJCPQzwRUhEUelULRbDOuEz22gKEjIyA3qcSbfG3NQUixPTNBqNF3Q1uml8MwCnnjxIMmzl4NOHyCysEmsy6VEkupNRDjTq+LEIo5t6SUU0VCEIXQ9VkkgnI6QSFkIIEokYo2OdZBYzpGIG59YqdHWmsAPBwYUSZTtgsClC1DC4+8QCZ7JVmvuG2bZtO6K5ha6xzbzvhhuRJImvfv5OJo8+SrLPwIhH6OuMc2JqBaPiMrdcZy1rk9ob8sEAACAASURBVK+4NCsRVM0joak0ySYlxSXUA+bdGg0poGrIJNqj3HjjDSj93ezcc8ULNnYXile04ANc/rqrmdp/GEkz6EWwks9SSiQYjKfxASdwcQiIoFIIHJKKjiJBqr2XiYV5rkq1cdNgK2dMB1cX6I0aA5E4R2dWeOjbh5hoO48Zi6Aj0yOiXJ4c4WxujUcba/z+H//tBQ2webEIgoCpyUnOPn0Mr1ynMD3N8vwSq0gke9opFqs0aQalXJlI1EJWZBamp/GWltkcTxLtNIhEYgg1pBaCoSpckWymWKvT6G/izTdfhheENDI5IpbB+ZUCKUXi8oEWBGBZOiXXIxYzcXyf5aVFLtu2g70PHeXcuXOMjo5e7CG6KJimSf/4GMe/8C2qS0sEvkuzHicQgmLgct6p0mdEyQYOxdAlLevIsJ6ZUoQgSXRqFqfyJQqFAr7vv+B93DS+maENw0xPTXF04TidcRNNCDojEeK6Rtw0iakSqgKxiEEibiIrMjUhCMIQnqk+p2kqyUSE1QWBbfv4Qci5lRK5SoNSw8MPBeeLDWaLBaq+oH9olP/+D59nePjf5nT6pY/+Kp/+ZJlzy6fpbVJJNyU59+1jSCWXbj/CJr2ZMAmeFxAGglzd4VxQYTyVJCpVOdrI0BaNUUknueGON/OGO95Kf3//y8Ib76Xfw+dJV1cXl932Bk4/8Bhu1EAcdbB1lUCRkYTAECqrgU1aNjAVhbxwMVEJNJW8ITObkAlGuvi/P/5faG9v59ixY2RWlnljqcxDX78be2KRFsegRTGp+R5fmjtNuTnC733qr7n22msv9uU/b4Ig4LHvfZ/6xBJDLZ2sei7LB05QyS9AdzuRgS3kdBWRL9AUayJ0PSqVCstnTtKjKvT2deAjcOshDcdHUlW8EHw5ZCCa4oy/nmwsHrcouQF+GHLi9DyXdCVoaUpQKlXRNHV95RAEyLKMokClXGJzc4RjRw6/agUfYGTTRj514r8yVha0yiaKkFjy6ijPRHa3qAYSEjN2BdmM0yxMPBFii5BQhFiSDJ7PUjZzwYqvGIZBT28v3R0tmM1pJueWWC7VSRgGhgRxRaFed5AkCMOAqusRClCVZ6LTBfiej+8FxOMmhXyN0fYENDwaxTpNEQMjGmWsJclmz+fLhxe444Mf+ZFiD+s5on75t36HP/zYb3D86HFmVrKcm86z1Ujhy5DzfAxk3EBgCw+QcH3BgVKWhh/SLOuIuMbY2BhveuvtP/Y8L0Ve8YIvSRJXX3cd0ViciYOHkVoSHNl3kLqp4oYBgaySijXh+T7CD4mpOg4S5ZKN3RLjyt/8Jd781tt/aIe/7LLLfnjsO97+do4cOsR937ib6eVVdMvk5utexy1vefMrJhf8qeMnsCdWGO8dZm5pgQe/8S0SXsjWQZmz1RXIRIl2DrCmyCytLhOurRGWc6iWy8b+YZq7W1ieX8MLAjy3jiZJREIfV0j0taVY9asUClViiQjp3naWMmWCMERBwnVcPC/A8wP8MKBSc2h4IRFLo9GoAgLXcS72EF00arUaX/vsP3JZ3wbWzk0SNDx0WabPiBGVVRa8OoGANa/OZitFQtE5Z5dJKutFR6phgCpJBIFHsVa+oHEOsixjWBYRw6DNMnnwxCyteyy64hbZmk1mKYs31kkJga/IBH5INK5Tt11CP6DRsHFtGwmZStlG7kpx/5MTtEgSPZ3NbO9p4ehCnvsm1rjunR/kfb/4wX+3P6lUiv/yp5/k1375nXzvyAK9IkqxHNBqKDSrBrqsYGkKAlhp1Kj6LoueQ7cSIzAkels6KKxmuOfv/oE3vv+9bBof/3fP91LhFS/4sJ7vZdfllzF+yVZmZ2fZv/RfaOnqYHRkM/qzHnLP96jZDQI/4OknHuQ3fvfXefs7fnyJwkgkwpVXX82VV199wXLVXEx832fi0Am2dHQjSRKPPfwIG40mHguOs3G4idf3p3j4VAbfk9D8ADsBqYE2Orq2Mvf4MaJalGrNJl9soCoKliFBPaQrEaHh+kimjlaDQqnGoKwwOtbH2RPTlGo29XwNw9CJhgLX9bAsk3oQ4gMiWE9ru5Avs7v71btpe/jpQ+iZMldu2cXnFhY5sjzH9fFOBvQYed+h4Dk8WF5kg5Gg14g+k6wZ8r5LVFEp+Q42AS4hsQicOnmC8S1bL0hfI5EIqfZuVg2LLZ2tTC5n+Mr+c2wabKNgu1Rck/1Hz7NhUzd+ENDeHCMRNSiXbSanl1lZWCVpwpOHZoiZBl/49gmcks2GlhRHyi5PHF5BTrXx+3/1Sa655pqfqk/5XI5Ws5laeY6UiCEaAYt2g9CQUCUJmwBdlWnVDBKhwaFynkvbWjjdKKHJKkndpCeUeOxb99Lb3/+S37CFV4ng/wDLsti4cSO/8P538Q9f+Q7DA8PwLMHXVI1UTGNq6gzRoM5tt932Ux/7lSb2ALlcDsMVmLrBSmYNeyWPkexESB7NTRY3XjFMvupSNlS6rxnFFhrVUKHmCKY1ldVSmWpd4NVdItZ6JGTS1Gk4Pp4XgO6DBLlMkZrjEk/F6R3uZiVf4pHT5zm1mCOuQLXmEElESaQTuI7HykqeWr7MRC3kP17x0t8ou1AcfPhxxpo6iMfjhIFAkxTcMGTaqRIQ0qzqzDhVAl2Q811MWUaXJebtOmlhYMgyR+sVkskoY53NTJw+zOjYxgsy05ckiZFLdjD51OMEc1N0J2OkUFg5t0ZVlVnxchyfXGZyepXxS/pwPZ+ZhSIz51dZWVjFlAOeWCpxZqLAttGNvOPWWzFUlYPzGW4d38b4+Dhbtmx5Tn2aOn4Cka1RLdus+DUus1qpBR4rDRtNBl2SCRyZw36NmFAxQ40Fu04ldKm5DmO9vcR0ndVMjpnJSbZu3/6Cj9sLzatK8H/AW++4g4mJKR6//xv0bdpOf/8GFE2nXC4wdfYE1ZVpPv6f/9NFKV7yUiIIgh/agvPFIinZoOE7IAtam2OYhs7P3biJL9x/ipN7j5LqbqPsCBq2wPHrHDw5x249TbeuILsSVd8nq8i4ukpLROdkLofeZSKV65w8PcvmsT6Ws0Ve/8bLmZ9f438+eYy3bGilsyWBLEnYtoPvhzS8kK8dn2TzWz5IJBK5yKN0cRBCUC+VSUY6KNQqtEQSFFWLhKaxwUwgA1FFo90ukvUd1rwGoRB4hNQCH0VInHFLmJZBtL2ZdtNE8iosLS3R399/Qfo8ODjI5utuYu/n/57+RJJ8ucRQxCKqKKzg8MjsKgdnMxw9dp6ugR5SyTit6SRdXcOsrRXp7ujn/bfvxtANMnWPeHc/v/X+K36mz6nv++SWlrFcn3LdQVbjrHgNLGRahIEmFBrCZy1ogIASHgLBKaeILQIms2u8fuduFEkmLissTE29JvgvVWRZ5nd+97fZfv/93PXNe3n0+H6Q1lOmXnXZpbzrd/6U3t4fX5rs1YJlWTTCdc8NRZEJECSMCIWMTcPxAIhYOh+47RLOzWf5X3cfZuJUhnZdp7/hUdF84rrP9s5Wqg2X5XKdcuhj+x6LFY/Jap6RDb1Yrsf0yVnyK3lKQciZY1OMt8V5tDXF504ssbPbZqApgeOHnF0tcSxT4UyuwW/fccfFHJ6LiiRJGNEI9YaDG7h0p1qZUSeJKybTToWYrBIJPZoUkzWvwaFqBimQicoqS16DinCJaxqO73L1xg1Ito9bKWPb9gXrsyzLXPeGNxFNpPjq3/2/+CWbo5kKhXKFmhtSQaF35x7eeMdbiJoSihzieR5+oPK6N24hmUpj2zayLDPW1va8TCiyLFMsFFG8kAgKWb/BTq2ZVi2CJCv4YUBSGHQpESb9EsecPKuiTkJRUeIapgGnl+e5pL0FTVbWPYpeBrwqBR/Wb/ibbrqJN77pTRQKBTzPI5lMvupn9c8mnU4T6WgiXy7S2dbBI7JPXLNQGjqHTi5xwxXDxCwdSZYZ7W9H5Gw+2NtJfyyCAtRsh+9OrPD1+fOMmnGaDI1cLWDGqZINbQabTeyFLBO2T0ZXsSWYz5dJyiG+pTI62MVa3yinclUOrRSQFIi0DdM61kbh7AJf/+rX+Njv/PZ6oerpaR567CGmp6eRkLh026XcevOtr5jN8x/Ftqsv58wXv8NQupW4YaKaJkJVSQkVRUDec8j5NhnHZpvSQlo3mfOqeJJgs5GmgMeK5LCcWWV0Qy+ZSvWCuxbKsszle/aw67LLOHHiBKdPncK1bZqam7l8zx5aW1sBcByHWq2GLMsXpJCOLMvosRh1P8CSNHqVKA1CPAQGAl1WCAnxQ2hWTBw5JO+7FGyHX959CYmIxSNzU+xUdpNt1Bh6mQQAvmoF/wdIkvSyLB79YnHJFbt44uvfZbytj66xIabOLHNFYhMPn36a+/ee47brNqPrMrMLRarZGl0bWgmDEFNXiagqt412s1KzmcqVWXJqrAUNVoXD+3YOki3XeSxbpkNTiCcsyq6PFdXYtXWAUrHKU0eWufzK7VyyPYqmariehxJK7D05x/DoJjIll0cffZRjp45x8MxBtLhGonU97uGuvXdx51fv5MPv/TBvf9vbL/IoXhgu37OHJ+6+j1a7jqNJDLZ0cSazzI3Jbhq+S7ussGrbbNda0JGZ82qcdyoYikZLJM4l0TjTfonpuVUWhtfINze/aOkqFEVh27ZtbNu27Ue+bxjGBXMT/QGDWzbx2J1fwpQUmhSDUJVYEnWSoYElK3hhQCa0qak+G9JpymbIyIY0E40Kza5LT2ecidV5gnSasc2bL2hfXyhe9YL/Gv8+nZ2d7Lz1eg4+8CjdwwPsm1ugtRjSXGrin75+hKW1Mq/bNcTMXI7QDnBdn4gqYzseCAlDVRhMRLi0LY0IBccXc3x3eYXhRBRXkrhlfICSv15b9JH5VZKtUdpSMQzTZMOlSTJrWSzTxLF9TGU9hUAyYiL5UQa7+/jbv/+fWO0SQ7uHGNkyQjwVR5IkfM/n7LGz/M2df4MsydzxCjT/xONx3vebv8Kdf/5X2NU8A62dZIt5Hi0vM6InyNYrNHyf86LCglMnCMGUVHqsBIqkYIcBPVqcmXqVJ6anuO36m1/WtXWfK3uuuoq/MVUCERI3LUxNw5IUiq7LWlDHlUI6WqKMJJo4Vs4x2NvCjbv66E1EufvAFNWKy7HsHLfe/vJxw35N8F/jJzIwMED7z7czMz2N0tvE/seewJz2SBcifOmuM9y/dwpdV1DKDWbWSgy3JrBMExGEWLqK03CQkfBEQM52UBWJsutS1nR2dzZzfK3MaqVBp2WSUBWWZtcoKgadPX2cPTJFrVxHNwwk08BzfAzVoNlKEQg4n1vl5tdfwaVXXoqs/O9lv6qpjO8cJ/ADPvXZT3HLLbdc8BnjxWB4eJhf+/gf8t177uXBO7+KkYySyeQ5VZylWKvSHUZJSwZNWEhC4EkhhXqdquMgIlF8TcIOXHKuzJtu+em90l4JJBIJhndu46kTU0iajG7p1ByXJtPC9lXiaY2IqZFxbIKYRH9PGllTMAyVG3YP8uW9U4zu2Mr2HTsu9qX81Lwm+K/xU2FZFpvHx9k8Ps5tP/c2yuUyCwsL+L5PuVzmiSee4MmvfZk1xWSjEUVRoGF7hKpEEAr8wKfueOxfzeGpgi+emCO5sR8jW2KuXGPAiBJRFExNQTQcSo6LlvJo72pB8gReEFKtuyiSiuNBb2crB8+cxYybDG0a+hdi/2zGd42z7zv7+MY3v8G73/XuF3nUXhxaWlr4+Q/8Im952x0ce/owd33ui9S+vx+/7NGKRbtskQ9dimEDVwgIwPQUlt0qlqyypNT4zY/+6isqyd9Py1XXXMMjX/omZ+p5bkgO0KQbFBybXNggbpm4OgSWTDwZwWyJYD2Tylu2dN5881WcXnFRFOViX8ZPzWuC/xo/E4lEgs3PslteffXVfCoMKe59iFMVh8v62gn8ImEYoCsKE5kye+czLNXq/NxYP0XbpaGpeE5AveEhG9ARs8h6DnE/RPcDKsUKIhDYBZtIJIqejpAtFFhcySPCGQ4eP4Rp1Tl76CRBINHe244VtTAjJpqukWhKoCgKHQMdHDh04BUr+D8gkUhwyc5LefBL32CorYtTSznygYPnBywLG0FIk2RgopILbbKBgyIEGd3n/PnzHD16lN7eXtLpNM8UNHrFs+XS7fSNDnLuxDRWYZl+M4GFgh7VEFEoyh6xVouCUqc1qpPWVeoipK2nDy3WzN7Tp15W+bJeE/zXeMF474c/zH89dhi9ludQpoBGSCVXwHEazGbKNMKQd4z1s6s1Td52mZJkCCEmq9Q9n9aERYdqcbZcpcVSOZ/J4HgqkUClmC0zfegsFWFiJNpYOr9EPpshUV3kxNoSQsAR2yMVj9E93EOyvxORjtM00oPjOGRzWSYnJxkcHHxZzcieK0/s3cv8oVPkzszSHViUggbTUoUOySImNAyhsCYaqJJMjxSlhofky3zrj/6Kxz/7FfouGWXzFTu54c23MDQ09IoX/pGREbbs2smDE1NMUSEwNLQANEMw0N2Krkqcd6u0DiVpURRMTcU1VDaNb+bp41Ok0m0vq+fpNcF/jReMlpYWfvtPP8lf/9nHmTx3mg2JCFUn4PxSjpgms6e3he6oSdnxaDZNjmVLRC0TBDRZBjXXJ6prqCjkbYEUlpmZzNOTbGe24lNPdhLVYrgipFJd5OrOFFv6h5G8MmNNEXqiFjNzGWYqdcTyKorT4Nzxc0ycXyLMFvnLT3yMptZerr7xVt7wxjc9J1c/IQRhGL6kP9z1ep3vfeMevEwJ1Vmf1XtSSJ8cIynpqMhMh2UEgm1yM31yHIBJUWLWrdPj6EgzGer6NN9Z/TxXvu1mdux+YSthvdRQFIWf/7Vf4dCjj+EsrjKp5knGovi2Q6xRJNpq0tWfJCoJ+uNR8gRs2bmTbL7MzGKJzVtfXtXXXhP8FwHf91lZWcG2bVRVpaOj4xXr79/f38/H//Kv2b9/H9/77r0c3v8YRhBwVWczcV2nGgRU/IDlWgOvXGdZUdGa4mRqDRwE8ahJxNKpeyFurkKb0+D4zDz6wHZkPYKhySydP8OOOFx26RiaJZM516BNN+npaKK9JYH7xBlmV/JouQpDSCytlIi1mTiFZXoGm9h3/z+zurLI+3/xQz9xBpvJZJg4eZr5c9MQCsx4lJHt4wwND7/k7uHk2XME5TqlUpkwdOknBrKMhEAVEg4BfVKMLjnCKjZ1fCxJo1OKgpCQax6OVMX0JdSyy5lHnqK9q5Pu7u6LfWkXlI0bN/ILv/c7/K8/+jjJhoNRtZkKazQUh01NabyajRUzma/XiXV1UawFSKrFYkHwgfe8vDLiviCCL0nSx4A/B1qFEFlp/VP0/wC3AHXgA0KIQy/EuV5OCCE4c+o0Zw4cJiiWEa5Hrlqh5Nvo6SRbx7cQTyUJwgC7VEGEglRbK0OjG0in0xe7+z8zpmly7bXXcc011/Lf/+gPKT3yXYY7mql5LkICh4CK7ZPSdFZn15hdytDSnqY9lSBTazBXrOLWXG7ZMMLQVRt5LOexKAwMBeYySwzFfLYMdNHTnuL0uRmaFI2pmQzlQpnQD2jTVBxZRdU1GnWX6wfacBMqDS/koccP8aH33Mz+k49z4MAl/yL76b/m1ImTnHn0AJ1mgsvahlEUhVqjzuJjx5g6eoprb3vTS8Z+G4YhU8dOISPj1xtEULAkjRIOUUnDQMVhvdZySfLQhcxiWGNESWKgYKFQrdbwVZNzM5O8rvly6jWHc8dOvuIFH+Ad73wnjXKF7/3jP+PkC2ySLKZO5hhKptja30bGs7Fau9m55xrqtsfdDx5kfNeNtLS0XOyuPyeet+BLktQLvBGYe1bzzcDIMz+XA59+5verioNPPcV37vwymdk5ipkc2C6y7RNUbaTA53u4oOvEW5po7+vh+tddQzRX47Gjp+jYMsauKy57WSdlkySJIAwxIhp52yZu6cSiBqapsliqsm9mDZ+QIVdm8cwidSuDqqvEhUJvWzM9zU3kajY5YdDd2UpmfpGphWl2tVm4jTqLC6ucPDrBNUNdGIkIThgSegFBKMD30TSTZNxgY3cH31ta49Y9A/DkBPc8vI+Brk4e//532L17N0IIXNddj758xlNlcXGRMw8/xSVdQz/MqBoEAcu5NTL5HNW5Gl8vf4lf+MiHXhL3yPM8nGqDqYlz+L5HXI6ho9AqRWiTTOKSTlm4JCSdjGiQFw7zYRWQaJFNImiYkky+HjC7uMDl23cS2g7Z2SV8339ZFPd4Pqiqyvs//CEGNmzgga99jezENJFiia8+McP3Z3PccP0uNsTb+db3DrGQc9l95Ru5/oYbL3a3nzMvxF38S+B3gbue1XY78E9CCAHskyQpJUlSpxBi+QU438uCe+65h0/+9u9DrkKTbNCKju6HmKGMIUkIVHqEQq0RkC2vILI2XzgzzaZd27j95lvJnJjksKKw8/LdF/tSfiRBELC2tobruui6Tlvbv9288n2fUrmCX7YpeCH98Tgza2VmfQdhyaTbo3THLBRP4M9kMTzobk7hCZAJeODAIbKyTKG5l1PZWVADmgdiGJJEqIRMzM6hKAGWDggXhABJUPU8bCFYWs6xrT1NueaQyVWZnMvTn4qxki0SeBUe+e43OTm1xGqxRs1br6o00NXGTdfuoZbJI+fL7M8fplavMbl4noXFs7SZIf1NMUSgMPN0jZWlGd73y//hJZF76dHHHyM7uYAlJOLoWJKMLqkkJAOBIESgITMgxdFkmSoeISHZ0MbGJx1YhFKIHkpMnJ+iffMGTGl99fBqQFVVrn/9jVxz/XXMz89j2zapVAqAM2fO4Louo8NN/Ny2bRe0dsCF5HkJviRJtwOLQoij/8oW2g3MP+v1wjNt/0bwJUn6CPARgL6+vufTnZcEnufxZx//BPd++h+J2QG7zFaWnCoRESIBA1qUqKKy6jeQJZkWWcLyZGrFGu11lTMP7uXsmbP88od+ibljp9i0dfyiZ4R0XZe1tTVgPb/O008fZP+jDxKU19AUCTOeIt7ay8ZLdjI6tpF4fD3a9YmHH0HKZLFMjXQ6wuGVLAdqJWLpCDHTIKaplCWBgqC7L83cWglMjb7mZux6nWylTtUAMyUYH9+C2ZZkZXGV6PIqoRfS2pHA8+pU6jU2j3QQ+OF6Cb8gwJlepRxozLouC8sOLU1JWtNphF/CyS+zLz/LuaxggmU6RraR6ujBcQUnC1ke+/++TLBygs0DMWzPp1KuM5TQuHlnL0OtrWhCIaYa5Es1zmYn+MJf/wXv+bWPXVTRP3bkCO5ChoiQcaUADQkBpCQdgaAm1s05EhIuIXFJoygckmjIskQzBrJYf37zjkejUKFkV2mNmC9bcftZURSFgYGBf9H2YqWcuND8RMGXJOlB4Edd7R8Av8+6OednRgjxGeAzALt27RLP51gXG9/3+atP/gWH7vwGI1KEuC5zspGjQzbxpYAmxSQf2lRChU4tSkP4WJKKrikcsnP0+zpV10FMrfGfP/EJ3v3e9zA3O8vGTZsuyvVUKhXuve9+9h4+RqBZ2E6DmePHiJbm2dEaIapINKSAyUKVmh3ymBVnw5adjGzbQaKjk+q5KQZiJrNFjf2rec7YVd71xnE2DTbjhOtfgLlyg9mFIk7RprU5yvRKFsPx0A2dY+USl9x0GcvFGt3DneQqdSxTYbleI2WZJOIWutEONY9spkJzc4xUwkLTVJZtB99zMbwAhEQ1DGlNxehuiXMoX+LE6TXkpkG2v+46CmsZrOI5IopE4FSQOppw1CHKhfPEjZBUROKK4VasqIavBUQti1rdpy0dQ7ZU8l7AvV/+PB/92H+6KPfJ932OPLafnlgzc0KgSTJ14ROV1233P5jJ24TU8NCRCYQgEAJdUiiHLkgaJd/mvKig1HQmZ8+TTMnc9NFfeMW7Zr6a+ImCL4R4/Y9qlyRpKzAI/GB23wMckiTpMmARePZ0p+eZtlc0+/fv59z9j9LpG8w0isSVKC2STo8axX9mOV0OXWphg9WgQUxWiSk6PXIEU5aY9avUQo9ROYVX8fjyP36OsC3F6NjYi24nLhQK/MWnP0M10sTI9beiaDqnjhwmXqlRWtL46unDJL0SEUVhS3OS3qSFLNvkjzzOaqPClCfwA4nxzhQnzzpM2FVuu36Uzo44jTAkHtGQQglVlUnGTOYWC8xM5jCjBh2pCIcWc/SMdCM8j6G+FvJrq3ghdHQkUcIe5o/PMDzUSr0kc255nrWSza64SSNTZTFTwOpOMpRq4v5HTrB5rIuR9hSnFjJEdY2GrNI/OoizqmHWFtkxnKCpdYBiuUJbk0bu6CGCyjIpSyVmKfS2xhltipKzHbIixPUCOtPNZHNVOtuakMrB/9/enUfLUd933n//auu9+/Zy91X3Srra0YKE0MYiVhmDDXaC93GS4zEnzjiPPWcmjJ85TzJnkhjssZ/M8/DE49hh7NixgzHYGAIGwiIESEiAtqv97vva+15Vv+ePbhzZxmAHSfdKqtc5fVRd3dL91K+vvl31q1/9itMjfQwNDc3LUerMzAxKwcSnGdQHaujLjTIrCoSkQU6UycoyXqERRMNC4kFlQubxCx2ERBEKNRiMyxx+YbBOrWcgm+GNQwcJhWsu+PY4zp9/cxWRUh6RUtZJKTuklB1Uum3WSykngMeAT4qKzUDycui/3/vzf8FKZMgVcoTQiCke6lQPmhDY0mbKymNKSa3moUnz0KT5sKXkYGkOE0mL5kNVFEwkTcLL0pLOG48+yYG9r1E5HXJhSCn5zg8eohhuYM3WHfhDISZHRxke6CORyVD012AsWUNSuFgWC1DrcxFUNKKqRp1LYaj3GFomTk16holkiv5EguaGAG2NIVYsrmPjqiaWtMVY0VVLd3uUSNCD2+OiqSnEVLnIdK7IJDahuhDjyQxunxtKGaJ+BWyT1vZ6ZEuEAwJhTQAAIABJREFU1wZGONk3SfvSZkqNIfaNz9AzlyQf8zNZMnn+xCDjikUo4sfj9zCSLzJaKNHQ3crirkY6fHGaIy4CwRDxeJKJ4WFGdz/HqhqbD92yjmu3LiccC6OVTRTLpi3sQ9qSklliNpNCcWsIXSfgM2jwqAwODl6wz+hslmXh1nVKisTv9pITFi6hMmhnmLTyWNgUpUVJ2iSsIvvMKQatFIZUELZC1jY5bicYt7P4pYpLCla56+i0vPzNX99/2fThXw7O16n3f6YyJPMMlWGZ73xH4UtAoVBgbmCEVCKFVjYJKDpNqpcD5WncioqNoFb1sEj3k5c202YeELRoPhpVL68Vp1ER+ITGFUaUN0ozlCwTY2SWgdfepK2z44L1I46OjnJ6fJpVN1Um08oXikyOjjA9N0WNz6Ar4qVU72cgOcmamiKLggFURYCsfFkkynBoboqVzc30DAxR0m2a64J0d8SIBNxggwAsS+I2dBa3hJlN5UjEc1ia4NjwFCOmiZ0tkCyUGJmKE4z58Xg0rEIRt64SDPsxPRrGYh8j8QxmU4hw1I8r4sVnGLi8Bvm+CSb3FsiYJlc0Rdh7ZBAj5CESi5KezLJl0xIsrUhiboZs0Wbo9Vf4w50ruWp9Fy6Pm3wmQyQWQE+lsLJ5cpkCXk1F1VRSuTz1EQ8WoKkKsrrt88Hj8eDye8noNsVikS41xLRVIILBaZkgiocQOiUspmUBi8qdr6atHMdEHI/QaBY+kJK0KJE2C5RzRVa3tdGz7wh9fX0sXrx4XrbNcW6ds4Jf3ct/a1kCf3yu/u2LgZSSfC5PsVjEIyGquBixMqx1RzhZSqEJwRpXGJ/QCVI5tBoyM3QrIRKyxAq9hjdLs3iFiktR6dQDTJp5yvkCIpmj9/jJC1bwjx0/gSsSw+X2AJBJp+nrP01jxMuajiiqJhiZTNPR2k5En2BZQ4jh6QyFsoVA4MLi6tY6XhscJ+Dzouoa0ZAXKcGlq9i2rAyhBFQBhqrS2Rjm0MnJyq35PDrmdJZ4KsuyhijT0wmaW2OV+fABn9sgNZumoS1Ga0cdLk2QnktxxepWhFCQ0qZUKJNI58mXTMrAiZkEBZfK+vWLOT2RoZhJ0dhai+KNURif5siRE2xa1si6NR3YUpLN5bEsC5ehkRUqq5e2cPToAC5FoShtbGxsoVAul8nkbMbSBTY1NV2Qz+dXRSIRAg212IAhBTHhJiHzZIXFcmpQhUIBi5BwsYQweVlmnzVJg+IlIAwKwkRF0KWECCkuxu08nrJKKZWkJRDhledfdAr+JWL+BxBfIlwuF3nFxqu7yEsTgSCiuNGEgl/R0FFQUShTGUlSq7oJKDpnyklSVgkpJC6hYqCSlxaaUGjUvSTLBXShMj184U6BFEoltLNGZkxNTeLWLBY1BIiGXEzMZsmky6hCYyJRRFEUmiI+hBCoikAgsbI57HyeuWQKVans0ReKJvliGcuWKAq8dS6wZNu4XBoTMxlE2SZjWtR4NOYGJlgSC6Gl85iFEkFdI2ToGEKgC5CKIJcpkJzL4HPraHpl/6VUspieSWFLwOUi0BIlUBeiuSGKLgRtYQ+ymKdYKKO6DDwuDWFmaG+OVrK5dAy3C5fPTTgaJF6yyJdNwjU+xicTvLrvNLv3nebRp15h98tvcOCNHsbShXmdS37x2pUk0ynqaqIoQtAsfPikylHmGLDTFKRJVpocsWc5ZM8QQCdFGQTUCDezssiYzGFh0y78zNpF5tJJfKpOcjYxb9vlOLcu7aspLiBFUWhZvpTy66cZkWWy0qJV0dCEoEH1UsRkzMqio6IIKEmLaTNPzjbxKRoh1SCmGBSkJC1LuIVKSHVRttKEQiGSF3BbamNRCodOYNs2QijEZ6bwGAq6Kjh0cpb4XBGvqpBJpKiRMDaaQXULfG6FoZkc6VwJu5xHKZscHZ8h7ZO4dJWpmQwhnwu/V8dlaCiqqAydB/rHE5wcmGGp20NaNYmEfcTnkrz65mlaW+s5c6iXmGcFgZAXyhZ+j4u58TnMXAGfrqBG/YyPzgJglkxymQJzMxliIT+xoI+RgWlWdtTTXOMnlSuQTqbIhALIQJo9u18nGjIolMuUzTJ6ScUwdISUqJpKR2cTe47206jC7GyWcFstVyxpxDZtjh4Z4oUDfdy2830cevYp8lt20NnVdQE/rYqmpia8IT9yOIupClyWRlAotEo/U3YeE0kJi5hw40JBFwptIkAJGxVwqyHSVglVVMbng41lWvTNTdB5kdzcw/HunIJ/Dm3bsY3ex1/AnBOkrCKWtHErLjKU8QqDWtVFWVZPoGERVlwEFIOCtPAJjQmrSLvuwyU0CtKiIE0Mnw/VpRNtunDjgFcsW4b32ReJT03hCYaYmZpAKVsM9mdo8/nxuFWS6RTJgTPMRFQQFkhIFkrUuV00udx4vCq6Cf1Jhf7pJBOzGSxpEwy4kNJHKl/C69KwbRiZSvK9xw/jcbnxt9VzbXMtWdMkODLDM2+cZu+xPoJNUaamE+guA0VTSMczdLTXk0/m0OoDZHWF/myBWDRAJl1gdHgGYUsMTTA6OkdIM4gEfEzMpDl6aoRyJkdPzwlmXz9KKOqnqTGCOxpgfDIO0sY2LSLRINH6CHX1XtLJLEd7BjmeyHHN9hXYhs5MuUTtxuXctWMtTz20l3XLlnPi1T3UNzRc8L19l8uF4fcSCIXoZxCvEDQpfrAlacVkjRrFj0ZSlkjJIq1KAIkkj0UZC5eElAJNeBkig4pCUVqkrAJrN1/aE6hdTpyCfw51dXez9tot5PI5xgZHWWwVCSk6IdXFUDlDreZCF5UTfKpl41cFedsiaZcYKWexpE2d5iFll9AUwbhVYOW69aSFxfqVF24sfigUYvuGNfz05ZfI4qGczKGbkpaQF5nP0nf6FNMDR9lR6yJU40FVIWeaFC2TRBGiboN8yaI3maEu7GOZKTl6bIKrruxg35ExGqM+musD2FIyPJHilcMjuP1+bl/TTEzTsVVIpUrkhWRZa5TRqQzmTBrbY4AqsHImrnKJTDzF1lVriWeyHD00Qr5UwjJtXEKhJRYknc2zf+9JgppGe0OMF14/w+jwDEGfi5a2Wl47M8ztH7oS1dB59okjlHU3s0KyfmkLdrlM72SK6VSBptoQ0RovtoDTloI5kgHLprk+TGdLBH/Qy4pNnXzlB//AJ265jf379nHt9ddfsM8LKkeYrauWcebAE9hI3IqOB404BTQh8KIhgVlZoEH48IlK8dcRGEKjIE00FHLCpJUAYzLHkJWjNtZI5xKn//5S4RT8c6ipqYnWNSu50+XnwX/8HkOJHH5bx6/omFhMmLlf7NEXMClZNkVpkrSKpESRmOqiJO3KCAq7TKHWR/eVawkv77zgV/rdfMMN/MtTz3DywB4msmlEPsmYzBMyLBaFDO5cFwMb8iULS5HgkgQVnalUgbGRArVuN1m/wsqOWoJRH4cGpxkdThGpCzCTnObY4DQaCn5dwacb1NeHiWgaBSyy2RIDvVP4bZXOznaiWyJYts1cOsdouUTrskbGhmcZGZrh4WcO0BTyEZEK7bVhbE2lYJkMjs0xM5fgozdtoKkpRmtjhPGpBFu727CQfOuZ/Vy5bRmxxjD9vVMotTEaV3QR9hqc7h+hPRakuauJVCLLq/tPMpfJ8+JAnIYdmygaBrlUntH+JM+82oeZy6OognQuzUOHDjDx1FPUfeMB7rz9A6xfvYbFnZ0EAoHz/pnd9P5dPP/9H6MgKUgbExsbiYogj4mOSlqWaVfcKFSLv1I5MS+rV+b60SliM06OhGbx4ffvwu/3n/fsjgvDKfjnkKIoXH3z9bxcfJKPffKTPPSd7+HKZAlbOioKx4sJajU39ZoXn6IzWk5iS5uCtClJC59iMGBmKemCeI2LxbfuYOWt17J2w/oLfrVjPp9n5nQv/mQf27wST0CwujnCVDrPsvogzUEviXwJr6GRLZRp8nooWTZD8SwvnJmmP1VkzbImPCEPdaogOJ1g5Mw0biNIOBwlPjOF7rGYiBfxx8KsjoYpmhaqAqdPTVHr9tHe2UCgJkBHRx2pRBZpSRom5zg6OEu0oQYrkSc3OMWp0+MsbavnSNnE4/cwPpthYnyaHWva6O6oZ3gqgdAVfC6dogJv9E9S0hQWLWkhPpchURRsvHEtZ46M0L24mejqpQxPzDJzfJRCrkjPYJK5gknT1esJNUQYH5wkW5QQjGIYIYoTU5QLOUy3SX8iztKtq1AMnb/75x9R+8IzfPCmXWxdu57upUvP62fW1dXFxg/cwrNf/RYBqXPGTOKTGm6hkpQlosKNBCwks7KIAgQwKGKToIgtbYrYTMgcSUp0LO1m2/UX1/S/jnfmFPxzrKGhgR0fvI2Dr77GTvPDvPqzJ7Fni4hiCU1TGbRyHC+nKAtJ0HAzYeUpqZIrmjspWGUyAYOOjVewa9dNXL/z+nm78fbPHnsM4r38weYOZuJpOqIemiMuTs+m2dgW4cUzUyytDdIc8jCdKTA6m6fB58Ll1ljWFmaiZLOyux6fz8A0bUIxHw8/d5y+kwOsXr+ZkpScPHGS9S0RVnY2EPN66JmLMzeRIOzzUlsbIljjJ1YbwlA1DMNAWjZLOhqYPJKjJhZkcniGD1y1gn/4yR60M+Mcs0yaVy3m2u1XE2hpZPfPn2XfyRE0FV4+1IuiGbS0t5AtCXIFk6JpMT6eINbWTl1dDYGtXt585RSGZlBfG0Jpqmfw+AjxUC1ajUXTkiZOvtELgRqibbVYKHijNZQKRSaPHCeQzuIPBfBFgizpbGTxkhZO7T3MI888ji0EHrf7vF6JqygKn/0Pf8yeR57AN1hkxkwybGdYooRAFtFVFQXBpMyjIogJD3ks4rJATpZpF0EUAUNmGs3v5errr6F7xfxM6+E4P5yCfx7U1tZy4+3v48rtW1i6fSOPf/t75PvHkKkcbkviNyVuRWHGLNASCrN1183YQQ+epjq2XLud9vb2eZ2O1jRNXv35T7hjTTN+oTAlYEl9kFShiF/XKJZtOiKVG5WXyjZeXUPXFeZKZWpCLpSAi2K8xOGZNDsWLcGtCgpeDy3L45zoG+fk8EmWdq5kfDrK4z3DyJYoawNeUiWLwak0m7pacPu9mCWbgLdykxEhJZqiYBgabdEQ47MZIvUhclkLd8jP9ESKXauuQOtawvBEjrWdEa5734c49OYhbEXwvjtvoqYmxPRshmMn+yhPjBGJ+uk9IwkZOtK2CAV9XHXtShLJAuMjM4yNznJqdI7YymWIxCyDxwexdA91izsolUz8tREUXUcChttL19Xr6X/lDcqKxnQ8Q2tTlPbudrJjk+w+sJe6QIjW1tbzerQWCoX4zJf+I4//9/+PwIiNnRMcLs4SFW6O2nEElQnVFokAo2QpSwsLiSIV5kSBITtL2pB0rVrG9bffumDm+3ecG07BP4/C4TC33normzZt4uXnX+Tw7leY6B8ikUoxrUHHim6u2b6DuuZGOruXLpibKfT09FCvl2iKBhmdiBP26fhdBrlyGdOWJPMlmms8ZIomedPCq6n43SpFGwI+F2+MZVixrpOasJ8lHbW4DI25ujze1jDP7D7OkWMTGE0+PnjDn/LMo4/y8rHTeDwBsiULoajkimWCYQ2FypBNJOQLJWr8XjQUagJe+uJxQgEvJ4dGUVVBTTSKe9EiulesxOhcQvfKlUgpufnGD1buaTszg23bLF7uJ9ZwmgNHXqRUtKiJhshkCoTCJtLOIxSFYI0Hj7+ZeCqPpyZEqKmeoTP9qIogtmo5mstA6gaKpiJtm3KugCtcg+ZyEe1sJzkyQmNjlGQig7cmQFSX7D/Qz0wmRTweJxKJnNfPb8t1O5jtG+bkk3tw940yO14ghhs3CmUpGZIZcpTpkEEEkKaEW+i4VIO8KJIIqHzqs59i87Yt5zWn48JzCv4FEI1Guf1Dd3LL7beRz+dRFAW/379gZyEcHhygIxbAF3ZTGpmpTB0A1HgNjk2U8RkqTeHqVbh5ExSVspRIRTCULhFrrWPN4nomUwXKto1q2UxkinR11WEpgpO901yxqp3BiRH+x//6X9z3V3/BqdlefHaBVKGEgYaZKxGOeSjkS+iaikdV0VSFkm1TLpuoLp1MKk/RskinimzYtInOFSspRWvZuGHDL03pq+v6L514fOaZp4lEghw7PMSadZ0cHU4ST2RxuXR0XaNQtpieTjNwZgLF7UeREgtBIVugORigVChhhKp7vhKwbIQiKp9rLExiYAihKWRTOXwulUg4SE3Q4Imnf85wz3E2btzITTfddN6O4qLRKNf9/h24fV4GXz1I8ZBB76le2oQfYUsaqFwXMkgGn9AIKW4UodAvUwzrRT7zV3/O3R//2HnJ5phfzpW2F5BhGIRCoV/MF79QabqOLSWt7W3obo3ZbJGibQOCxpCX/tkM2aKJLcGUNgVpUQZSxTIn4gVqY0FCHhf5kkU6W+LkZIK69jCqqhAL+1i0qJ58rkhibgy3280X/9OXCIQ7KOMmLW0MFHKZAgYK4+NzZFI5gn4PhqKgCxhIpgmEPExMxEkksqjuGro2b6HhqqvZtvOGd52/PZfPsfqKJWjS4PVXT1KYTRIMB7EQzMWz9J0YZd+Lx0jmLAy3i+lT/bSvWw6KRiFfJJ8rYFsWZtlCIikXCygKKJpaudG5oqAgMC0LTBOVMrZtMjx0jL6B3fzd3/93rtt5FV/72lfP22fY3t7Ork/8Htfc8zG677qBTJ2XN5UEh5Ukp0SKjGLjVnU0TWdOlDihpLBawnzoC/+eT3/6kp/66rLl7OE7fs3iJUt54zETKRTWb7yCR596gaF4ho6wj9awj4F4hmdOTrCyMURDyI1p22RKJscm0qRCIWoiXo6MzTGWKxDyhenqasTn1ekfSYBQCAT9WJaNqiqYpsnRo0c5vHsv/adP4on62V2AxbEw6WSOxvoaPJqOHaxMx9A3PkfOrTLbP8X4eILxE5O8/1N/yB0f/dhv/SUaDAQpZTXu/NBOXnjuAPv393D08DCxRU2UTQvDMLh6+yr27DnG7JkhdE2hvr0Zl8vAMi0MnxfLrFyFbFsmpUwOw6j05edmE2i6gm5oJHNFsCWeSIBcrsgN1yzj8/fcSCZTYM9LPfzoBz9icnKS++77ynn5HIPBIJu3Xs3mrVdz41238937H2D81BnMeIZ8NkeibGJh4YtF2LppA+G2Rj7wmX+3IG7Z6Dg/nILv+DWdnZ2IWDsHTg1y7dplbN+ykT2vHcLlNmgMutm2pIF/3NfLEz1jLIr4mM4UiRdMRjMFVjfX0dAUYiSe49r17dSEPBSKJgOjCQxDY2ykcrUsChjuIP/0D//Ag1//OsbcHJ2GGz1tkZhJMYagORIiq6ZJpfIcOz1M3LaYFTbT6RzDZ8bJjCZY09bFpu3bf6cjpmuuuZa/vP8Jdt2+lQ9/5EZu3rWFEz0DHD81jOr34Q8HScVTFAfGqfNFsUNeZCKF1+ch0TeMsaqbYiqDomtkpmfxhoOotkVieILU2Dix2hpS8SS6WSDm9dJzfIzhwUmu39hC/8A07e213PHBLTQ2Rvirv/gnHnywmxtvvIWmpqbzVmy7ly1j553vQ5tMUyoWyeRzSClpqWvA5/YwnpzDv7yNxsbG8/LzHQuDmK8pXd/OlVdeKQ8cODDfMRzAwMAAf3fff2Vrk8GWlV0MTc7ywv4jBDULDZsjw7OcmkyjKZJavwuXppG1TZKawY7rVrFyZTNtTWHKpk3JtAgFvEzN5TjYM07JdGEVNWZmVGbePMjYqZNc4QsQL5ewJOiqQmtHHUmfiu7SMTQVU4FUqczkZILsZIpUPEtzUxO1y5dx7wMPEPod53v56lf/GldojrvuvhFV/dcim0pmSaWyPPbjFwi6u/joRz7JV/7m6xwcOInl1RnrGyHQ3oLu9xGojxFrb6JsWdjFAuNv9KBrKi2dzbhLRRr8gtlEjuMnhlnWVcvtv7eNbDxFamKWVUvq6Oqq43/c9yg/+/F+br7pBtZtuJr3337XeTupWygU2Ld7D7N9I8Q0L4aqkS+XmJNFWld3s+Gqjb92X2LHxUEI8bqU8l3nwHAKvuM3Ghwc5IcPfpNEfw9tIQMpJfv7JsirfrbtvJkt27YTTyQ4dewwdrFAJBbjJ08+QTRc4IrljSxujxKu8SElDI0lmY0XUF0+psbTpDNumguCx594gm0+L6qsnP+cLhSIuVwEdQOf12BAtcgqEkuCO28SKUn2z8yCrtPY0cHaj9zNpz7zmd9523K5HH99359j+LNs3rqCpcs6ADjW08fel3qQpQj/53/5cwzDACojlx5/8p957uXdDE2NE2xpxlsbQXO7sKTFVN8Aml1Cy+WI+dwE/C7Gk0UKxTIdnTF23ryeWH2YusYolm1z5uBp1i+JMTgwxZf/2z9x93XLefPkFKanmW/+3XfP63DIRCLB6MgIpUIRj89La1vbvM706XjvnILvOGdGRkY4deoUlmWxaNGid5wbPZPJ8O2//1tSiV6wMvi8KpqqomoGuuFmdCyDP9BOVPMRP3KYw7tfYmMohKEotHq8vDg9RY2hE9IMdEWhwe3CUBRKtiRnmRxPpxnMZLAMHc+VV3LfN77xO+/dv6VcLvPss8/w4p6niScmkRKikUau23ELO3fufNu93XK5TE9PDw//9FGOnj5BvlzCLpssW7wUM1/kwN49qKrJ6EycmroQGzcuoq2zHmybfNFCKBorNyylsaWOsZ7T1IVc/MV//QG7//aPGJlK8MD3X2KsEOP7P3hkQZ/YdywsTsF3zJt0Os3rr+9jdKwfRJFkIk6xUMY0NbZsuZ729g7efPxxfv7II7iGhmlxuVni91NjGBxNJRjKZunwBTAUQc6ysKRESkmyXKY/mwEUelTBE/v2nbNrFypTQYvfushKKclms5RKJTweD7Zt8+RzT5N32ex56SVOHdrLze9fy3W3rsPjdmFLm+nxOKeODjM6mmDVhm6sQomJkwM8/P3dPP3Apwn6PYxMzvGfv/YUH/3sX3Dbbbedk21zXPp+24LvnLR1nHOBQIBrr72BeDz+iwueAoEADQ0NKIrC6Ogobl3HMk1MKSuTdmkablWlyeOlRjN4PT5HvduDV1FIlstMFPJkTJOoy0VvqciOXe8/pxeq/a4nS4UQvzS2/82Db5JTLdKlPAPHD/HhP9hJKGSQzxXxuF0oQqG2MUw44ue1Pcc5fvAMbYtbeWXPcTYsrSVRHXraUh9h08omHnv0+07Bd5xzTsF3nDfhcJhwOPxr6/1+P6amEY5EGRkeptYy0RSFomVR53IxaUve39TM4WSc2WKJhFlmccDPSC7PaKlEMBrjultvnYctenu2bXNmqJ+ybjHc20tre5hFS1vIZXNMTyUxDB2/340ANJfOkuUtjAwd5uknDzDUO87/9aX3k8mXiCdzGLrKopYo+04MUCqVfnEOASCbzfLEE0+QyWRYsmQJ27dvn7+NdlyU3vMYMCHEnwghTggheoQQ95+1/l4hxBkhxEkhxM3v9ec4Lh2hUAh3bS3LVq3C0g1G8nlSZhkLEAhavF7mSiW6/UHqPW48qspMsURfNoM74Ce2uItt27bN92b8QrlcJpPPoro0xocG6FrehrRtIvUxLATDQ9OMjMyQSuXJZvIITSWRyHB07zHWdUZ59MkjHNzby6F9Z3jh+R4O94xg2zblchmoFPrPfe4ePv7x29i//6cMDb/Cd7/7Fe6661a+853vzPPWOy4m72kPXwhxHXAHcIWUsiiEqKuuXwHcDawEmoBnhRBLpZTWew3suDSs2riRxNg4dZ2LGOg5xpPj49zS0EBZ1fBrKjHD4HAyyZlMGr+mcyabRhgu2pYvZ8enP72gJvVSVRWzbKIrAmlb6C4NXdWwSmUiDfVYhQLJ2QSJeBYpJbZlk8/kKYzNMTiTIeHW6HMbbFzXwc2bFzNuqwxZgpHhIVrb2vnEJ36PTZta+eIXP0d7R2WcvGVa7N13lAcffJjZ2Vm+8IUvzHMrOC4G77VL5x7gy1LKIoCUcqq6/g7gh9X1/UKIM8Am4NX3+PMcl4ja2lqu2nUrM5k0+Vye3r5e/vfgAC1uD6qiIJD4FI2wbjBSLFLwetl03XVsuO02rrnhhvmO/0s0TaOtoZmjE32EaiJMjc6hb3VRyOVAVVA1nVhDjFQ8SXomiSiXSQzPsM7tYnNDBJ+uUjJtet8c4j+/coqutlrWr9xE3/59/L8PPMC69U184YsfQ9P+ddSQqqls3XoFTY0x7r33m/T23kHXPNxL13Fxea8FfymwXQjxl0AB+I9Syv1AM7D3rPeNVNf9GiHEZ4DPAOd1rnDHwtPU1MSnP/c5Vm/ezFM//jGHdu/m9NwcYctEEQqWKim63ISWdPHhO+5g6w030NLSsiAv/b9i1Rpee+ggzZ2LOXlwD0N9Eyxd2UY+kyM+G6eQzVLM5PBbFiP9kxR6p/n8inY6gj5sCfFimdVlk750lif6JinMPMfhNw9y2q3w7z/7J79U7M+2qLOZa65Zw9/+7QN89atfu8Bb7bjYvGvBF0I8C7zd/fW+VP37EWAzsBF4SAjR+bsEkFJ+E/gmVIZl/i5/13Hx0zSNzZs3s3nzZrLZLCMjI4wOD1MuFNBcbjoXd9HR0bHgx6Q3NDSwY91mfvbS05SLBvtfPoYioGNpMyoSK5UhapqkZzK88ORBbvR6WRMNoQAWoAmYlrA8HCBnWhycSxNLzxJrbKWpMYJlSVT17dvgyo3LeOmlxy7o9jouTu9a8KWUv/H4WQhxD/CIrAzmf00IYQMxYBRoPeutLdV1Dsdv5PP56O7upru7e76j/Jtcs30HXreHh372CEeOvs5Y/yR1dX78boHLNEmNJzh9dIR1JcnmjsZfjJhQAV1VsSli2tAV8HFgOomwJIauUMqkyWsq/t9wX1yXy0BK+4Izp/2mAAAJVElEQVRtp+Pi9V67dH4CXAc8L4RYChjADPAY8I9CiK9ROWm7BHjtPf4sh2PB27hxI6tXr+bIkSO8+PJuDh58jdffeJ1aaXKlobOyZHFjeyO6IijZEk3ASLbAXKlMuVq0bSFRBWTLFhMTcVJzSVSXC4/X+7ZX//b2juDx/NuuNnZcXt5rwf974O+FEEeBEvCp6t5+jxDiIeAYYAJ/7IzQcVwu3G43GzduZOPGjYyNjfHfPvtHNE8Ps6HGywtjs0gpCegac8USM4UiLl2lO+zFBiwpSRVNvIbKbKHI2OkRdr/awx23RyiXyqieXy74xWKJZ595nbvucuawd7y793T2S0pZklJ+XEq5Skq5Xkr53Fmv/aWUsktK2S2lfPK9R3U4Lj7hcJhIQwMThTLpsknYUOlNZQnoGoOZPIamsKzGS8Co7HtpiqBgWfh0lVWxIDHL4oc/eI59B05g/0q3TbFY4v/5nw8Rj+Nclev4rThX2joc55HH4+GK7dfy4okehjJpuoI+dk/MsTYWQlcEtR6DjGmhIChaFmXb5sBMAkWV3NAaYTBTYG5slvu//ENe2HOMa65Zi9frZmRkiuefe5Ny2cu3vvXdBX9S27EwOAXf4TjPdt66i55XXuLgc8+iADGXzoMnh7mqIcRq3U9ZSibyBabzZXpTWaYKJa5vjlKWktaAC1RYuW4LwbolPPyjN7DsMsFAhD/8wz9zpldw/E6cgu9wnGexWIzf/+PP8+1Ekpde3U2NoTFXKDIzWOB4PINPr/bLC4ktJetqA3SHA/SmsvgNnZlkgV07ruPOuz8yvxviuOgtvCtYHI5L0MpVq/jiV79O3VXb6c+VSRZNAoZCjUega5KgC9qDbrY3h1kTDVAwLfKWzXTRRGvrZNcHPjjfm+C4BDgF3+G4QJqbm7n3y/ez8867WNbZwVTBIugyWBUNsjIaojPkxa/rzJXKzJUt4hb0mjr3/tX9uN3u+Y7vuAQ4XToOxwW0aNEibvr9j/JCuUjnZB2zc5N0uVxE3TpSSoQiMFSdwUyOQwWFGz79WTZs2DDfsR2XCKfgOxwX2Oat27AlvPnPjzFx+hgvzk1Sq5XoCPvRURlKFzkjvWz4xEf4/Y9/ar7jOi4hTsF3OObBlm3baG5t5dj+fcwODzE3NsyBqUnytqR10yru+fDdLF682Blu6TinnILvcMyT9vZ22tramJ2dpVAoIISgrq4OXdfnO5rjEuUUfIdjHgkhzum9eR2Od+KM0nE4HI7LhFPwHQ6H4zLhFHyHw+G4TIjKbMYLgxAiDZyc7xy/pRiVuf8XuoslJzhZz4eLJSdcPFkXYs52KWXtu71poZ20PSmlvHK+Q/w2hBAHLoasF0tOcLKeDxdLTrh4sl4sOd+O06XjcDgclwmn4DscDsdlYqEV/G/Od4DfwcWS9WLJCU7W8+FiyQkXT9aLJeevWVAnbR0Oh8Nx/iy0PXyHw+FwnCdOwXc4HI7LxIIo+EKItUKIvUKIg0KIA0KITdX1QgjxP4UQZ4QQh4UQ6+c7K4AQ4k+EECeEED1CiPvPWn9vNetJIcTN85nxLUKILwohpBAiVn2+4NpUCPGVanseFkI8KoSoOeu1BdWmQohbqlnOCCH+bL7zvEUI0SqEeF4Icaz6e/n56vqIEOIZIcTp6p/h+c76FiGEKoR4UwjxePX5IiHEvmrb/pMQwpjvjABCiBohxMPV39HjQoirF3K7viMp5bw/gKeBW6vLu4AXzlp+EhDAZmDfAsh6HfAs4Ko+r6v+uQI4BLiARUAvoM5z1lbg58AgEFvAbXoToFWX7wPuW4htCqjVDJ2AUc22Yr7br5qtEVhfXQ4Ap6rtdz/wZ9X1f/ZW2y6EB/AF4B+Bx6vPHwLuri5/A7hnvjNWs3wH+KPqsgHULOR2fafHgtjDByQQrC6HgLHq8h3Ad2XFXqBGCNE4HwHPcg/wZSllEUBKOVVdfwfwQyllUUrZD5wBNs1Txrd8HfhPVNr3LQuuTaWUT0spzerTvUBLdXmhtekm4IyUsk9KWQJ+WM0476SU41LKN6rLaeA40Ewl33eqb/sO8IH5SfjLhBAtwPuAb1WfC+B64OHqWxZEViFECNgBfBtASlmSUiZYoO36bhZKwf9T4CtCiGHgq8C91fXNwPBZ7xuprptPS4Ht1UPPF4UQG6vrF1RWIcQdwKiU8tCvvLSgcr6NP6ByBAILL+tCy/O2hBAdwDpgH1AvpRyvvjQB1M9TrF/1f1PZGbGrz6NA4qwv/oXStouAaeDBavfTt4QQPhZuu76jCza1ghDiWaDhbV76ErAT+D+klD8WQvwelW/TGy5Utl/1Llk1IEKlO2Qj8JAQovMCxvuFd8n5X6h0lSwI75RVSvnT6nu+BJjA9y9ktkuJEMIP/Bj4Uyll6uw7ZkkppRBi3sdhCyFuA6aklK8LIa6d7zzvQgPWA38ipdwnhPgbKl04v7BQ2vW3ccEKvpTyNxZwIcR3gc9Xn/6I6mEeMEqlH/otLdV159W7ZL0HeERWOu9eE0LYVCZTuuBZf1NOIcRqKnsmh6r/4VuAN6onwxdcmwIIIf4dcBuws9q2ME9Z38FCy/NLhBA6lWL/fSnlI9XVk0KIRinleLXrbuo3/wsXzFbgdiHELsBNpTv3b6h0L2rVvfyF0rYjwIiUcl/1+cNUCv5CbNd3tVC6dMaAa6rL1wOnq8uPAZ+sjizZDCTPOoyaLz+hcuIWIcRSKidxZqhkvVsI4RJCLAKWAK/NR0Ap5REpZZ2UskNK2UHll3a9lHKCBdimQohbqBze3y6lzJ310oJp06r9wJLqaBIDuLuacd5V+8C/DRyXUn7trJceA966E/qngJ9e6Gy/Skp5r5Sypfq7eTfwnJTyY8DzwIeqb1soWSeAYSFEd3XVTuAYC7Bdfyvzfda4ujO3DXidyqiHfcCG6noBPEBlZMQR4MoFkNUAvgccBd4Arj/rtS9Vs56kOupoITyAAf51lM5CbNMzVPrGD1Yf31iobUpllNOpaqYvzXees3Jto3Jy/vBZ7biLSt/4v1DZiXoWiMx31l/JfS3/Okqnk8oX+hkqR/qu+c5XzbUWOFBt258A4YXerr/p4Uyt4HA4HJeJhdKl43A4HI7zzCn4DofDcZlwCr7D4XBcJpyC73A4HJcJp+A7HA7HZcIp+A6Hw3GZcAq+w+FwXCb+f7UWSVBFtIIIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emb_transformed[\"infomap_clusters\"] = emb_transformed.index.map(infomap_com_dict)\n",
    "plt.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"infomap_clusters\"],\n",
    "    cmap=\"Spectral\",\n",
    "    edgecolors=\"black\",\n",
    "    alpha=0.3,\n",
    "    s=100,\n",
    ")\n",
    "plt.title(\"t-sne with colors corresponding to infomap communities\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we apply dbscan algorithm to cluster the embeddings. dbscan has two hyperparameters: `eps` and `min_samples`, and produces clusters along with the noise points (the points that could not be assigned to any particular cluster, indicated as -1). These tunable parameters directly affect the cluster results. We use greedy search over the hyperparameters and check what are the good candidates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eps:0.1\n",
      "eps:0.2\n",
      "eps:0.30000000000000004\n",
      "eps:0.4\n",
      "eps:0.5\n",
      "eps:0.6\n",
      "eps:0.7000000000000001\n",
      "eps:0.8\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_dt = utils.dbscan_hyperparameters(\n",
    "    node_embeddings, e_lower=0.1, e_upper=0.9, m_lower=5, m_upper=15\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_clusters</th>\n",
       "      <th>n_noise</th>\n",
       "      <th>eps</th>\n",
       "      <th>min_samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>0.4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>71</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>117</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>124</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>129</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>0.3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>289</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>348</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>379</td>\n",
       "      <td>0.2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>416</td>\n",
       "      <td>0.2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>452</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>495</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>541</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>593</td>\n",
       "      <td>0.2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>629</td>\n",
       "      <td>0.2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>656</td>\n",
       "      <td>0.2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>1450</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1571</td>\n",
       "      <td>0.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1647</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1728</td>\n",
       "      <td>0.1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>1824</td>\n",
       "      <td>0.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>1884</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1910</td>\n",
       "      <td>0.1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2060</td>\n",
       "      <td>0.1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_clusters  n_noise  eps  min_samples\n",
       "35           2       38  0.4           10\n",
       "20           5       71  0.3            5\n",
       "21           3       91  0.3            6\n",
       "22           3       94  0.3            7\n",
       "23           3      102  0.3            8\n",
       "24           2      117  0.3            9\n",
       "25           3      124  0.3           10\n",
       "26           3      129  0.3           11\n",
       "27           3      138  0.3           12\n",
       "28           2      151  0.3           13\n",
       "29           3      160  0.3           14\n",
       "10          16      289  0.2            5\n",
       "11           9      348  0.2            6\n",
       "12          10      379  0.2            7\n",
       "13           8      416  0.2            8\n",
       "14          10      452  0.2            9\n",
       "15           9      495  0.2           10\n",
       "16           7      541  0.2           11\n",
       "17           7      593  0.2           12\n",
       "18           7      629  0.2           13\n",
       "19           6      656  0.2           14\n",
       "0           37     1450  0.1            5\n",
       "1           27     1571  0.1            6\n",
       "2           23     1647  0.1            7\n",
       "3           22     1728  0.1            8\n",
       "4           17     1824  0.1            9\n",
       "5           15     1884  0.1           10\n",
       "6           15     1910  0.1           11\n",
       "7           15     1966  0.1           12\n",
       "8           13     2012  0.1           13\n",
       "9           13     2060  0.1           14"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print results where there are more clusters than 1, and sort by the number of noise points\n",
    "db_dt.sort_values(by=[\"n_noise\"])[db_dt.n_clusters > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pick the hyperparameters, where the clustering results have as little noise points as possible, but also create number of clusters of reasonable size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform dbscan with the chosen parameters:\n",
    "db = DBSCAN(eps=0.1, min_samples=5).fit(node_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the clustering statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 37\n",
      "Estimated number of noise points: 1450\n",
      "Silhouette Coefficient: -0.054\n"
     ]
    }
   ],
   "source": [
    "labels = db.labels_\n",
    "# Number of clusters in labels, ignoring noise if present.\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "n_noise_ = list(labels).count(-1)\n",
    "print(\"Estimated number of clusters: %d\" % n_clusters_)\n",
    "print(\"Estimated number of noise points: %d\" % n_noise_)\n",
    "print(\"Silhouette Coefficient: %0.3f\" % metrics.silhouette_score(node_embeddings, labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot t-sne again but with the colours corresponding to dbscan points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 't-sne with colors corresponding to dbscan cluster. Without noise points')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emb_transformed[\"dbacan_clusters\"] = labels\n",
    "X = emb_transformed[emb_transformed[\"dbacan_clusters\"] != -1]\n",
    "\n",
    "\n",
    "plt.scatter(\n",
    "    X[0],\n",
    "    X[1],\n",
    "    c=X[\"dbacan_clusters\"],\n",
    "    cmap=\"Spectral\",\n",
    "    edgecolors=\"black\",\n",
    "    alpha=0.3,\n",
    "    s=100,\n",
    ")\n",
    "plt.title(\"t-sne with colors corresponding to dbscan cluster. Without noise points\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating GSEC and infomap qualitative differences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the resulting GSEC clusters, and explore, as an example, one particular cluster of a reasonable size, which is not a subset of any single infomap community. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display cluster sizes for first 15 largest clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "-1     1450\n",
       " 4      337\n",
       " 8      304\n",
       " 9      255\n",
       " 3      230\n",
       " 0      142\n",
       " 11     138\n",
       " 7      106\n",
       " 1       80\n",
       " 24      70\n",
       " 26      67\n",
       " 18      39\n",
       " 23      32\n",
       " 15      31\n",
       " 13      22\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustered_df = pd.DataFrame(node_embeddings, index=node_ids)\n",
    "clustered_df[\"cluster\"] = db.labels_\n",
    "clustered_df.groupby(\"cluster\").count()[0].sort_values(ascending=False)[0:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to display clusters that differ from infomap communities, as they are more interesting in this context. Therefore we calculate for each DBSCAN cluster how many different infomap communities it contains. The results are displayed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "-1     150\n",
       " 0      18\n",
       " 1       3\n",
       " 3       9\n",
       " 4      17\n",
       " 5       2\n",
       " 8       4\n",
       " 9      16\n",
       " 10      4\n",
       " 11      3\n",
       " 12      5\n",
       " 13      3\n",
       " 14      4\n",
       " 15      2\n",
       " 17      3\n",
       " 19      2\n",
       " 20      2\n",
       " 22      3\n",
       " 24      9\n",
       " 25      2\n",
       " 28      3\n",
       " 30      3\n",
       " 31      2\n",
       "Name: infomap, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inf_db_cm = clustered_df[[\"cluster\"]]\n",
    "inf_db_cm[\"infomap\"] = inf_db_cm.index.map(infomap_com_dict)\n",
    "dbscan_different = inf_db_cm.groupby(\"cluster\")[\n",
    "    \"infomap\"\n",
    "].nunique()  # if 1 all belong to same infomap cluster\n",
    "# show only those clusters that are not the same as infomap\n",
    "dbscan_different[dbscan_different != 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, DBSCAN `cluster_12` has nodes that were assigned to 2 different infomap clusters, while `cluster_31` has nodes from 8 different infomap communities.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single cluster visualisation\n",
    "\n",
    "Now that we've selected a GSEC cluster (id=20) of reasonable size that contains nodes belonging to 4 different infomap communities, let's explore it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__To visualise a particular cluster, specify its number here:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify the cluster id here:\n",
    "cluster_id = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Supporters of Muse Sudi Yalahow',\n",
       " 'Jemaah Islamiya (JI)',\n",
       " 'Bangsamoro Islamic Freedom Movement (BIFM)',\n",
       " 'Magahat Militia',\n",
       " 'National Democratic Front-Bicol (NDF-Bicol)']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manually look at the terrorist group names\n",
    "list(clustered_df.index[clustered_df.cluster == cluster_id])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a subgraph from the nodes in the cluster\n",
    "cluster_G = G.subgraph(list(clustered_df.index[clustered_df.cluster == cluster_id]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List for each of the `gname` (terrorist group name) in the cluster the assigned infomap community id. This shows whether the similar community was produced by infomap or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Supporters of Muse Sudi Yalahow': 23,\n",
       " 'Jemaah Islamiya (JI)': 21,\n",
       " 'Bangsamoro Islamic Freedom Movement (BIFM)': 21,\n",
       " 'Magahat Militia': 21,\n",
       " 'National Democratic Front-Bicol (NDF-Bicol)': 21}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = {\n",
    "    k: v\n",
    "    for k, v in infomap_com_dict.items()\n",
    "    if k in list(clustered_df.index[clustered_df.cluster == cluster_id])\n",
    "}\n",
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another metric of clustering quality, we display how many edges are inside this cluster vs how many nodes are going outside (only one of edge endpoints is inside a cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "external_internal_edges = utils.cluster_external_internal_edges(G, inf_db_cm, \"cluster\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>nexternal_edges</th>\n",
       "      <th>ninternal_edges</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>136</td>\n",
       "      <td>6</td>\n",
       "      <td>0.044118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cluster  nexternal_edges  ninternal_edges     ratio\n",
       "14       20              136                6  0.044118"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "external_internal_edges[external_internal_edges.cluster == cluster_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the structure only\n",
    "pos = nx.fruchterman_reingold_layout(cluster_G, seed=123, iterations=30)\n",
    "plt.figure(figsize=(10, 8))\n",
    "nx.draw_networkx(cluster_G, pos, edge_color=\"#26282b\", node_color=\"blue\", alpha=0.3)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that terrorist groups (nodes) are connected, when at least one of the attacks was performed in the same decade in the same country. Therefore the connectivity indicates spatio-temporal similarity.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are quite a few clusters that are similar to infomap clusters. We pick a cluster that is not a subset of any single infomap community. We can see that there are disconnected groups of nodes in this cluster. \n",
    "\n",
    "So why are these disconnected components combined into one cluster? GraphSAGE embeddings directly depend on both node features and an underlying graph structure. Therefore, it makes sense to investigate similarity of features of the nodes in the cluster. It can highlight why these terrorist groups are combined together by GSEC."
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       "            background-color:  #006837;\n",
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       "            background-color:  #006837;\n",
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       "            background-color:  #006837;\n",
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       "            background-color:  #006837;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >n_attacks</th>        <th class=\"col_heading level0 col1\" >n_nperp</th>        <th class=\"col_heading level0 col2\" >n_nkil</th>        <th class=\"col_heading level0 col3\" >n_nwound</th>        <th class=\"col_heading level0 col4\" >success_ratio</th>        <th class=\"col_heading level0 col5\" >Armed Assault</th>        <th class=\"col_heading level0 col6\" >Assassination</th>        <th class=\"col_heading level0 col7\" >Bombing/Explosion</th>        <th class=\"col_heading level0 col8\" >Facility/Infrastructure Attack</th>        <th class=\"col_heading level0 col9\" >Hostage Taking (Barricade Incident)</th>        <th class=\"col_heading level0 col10\" >Hostage Taking (Kidnapping)</th>        <th class=\"col_heading level0 col11\" >Airports & Aircraft</th>        <th class=\"col_heading level0 col12\" >Business</th>        <th class=\"col_heading level0 col13\" >Educational Institution</th>        <th class=\"col_heading level0 col14\" >Food or Water Supply</th>        <th class=\"col_heading level0 col15\" >Government (Diplomatic)</th>        <th class=\"col_heading level0 col16\" >Government (General)</th>        <th class=\"col_heading level0 col17\" >Journalists & Media</th>        <th class=\"col_heading level0 col18\" >Military</th>        <th class=\"col_heading level0 col19\" >Police</th>        <th class=\"col_heading level0 col20\" >Private Citizens & Property</th>        <th class=\"col_heading level0 col21\" >Religious Figures/Institutions</th>        <th class=\"col_heading level0 col22\" >Tourists</th>        <th class=\"col_heading level0 col23\" >Transportation</th>        <th class=\"col_heading level0 col24\" >Utilities</th>    </tr>    <tr>        <th class=\"index_name level0\" >gname</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122level0_row0\" class=\"row_heading level0 row0\" >Bangsamoro Islamic Freedom Movement (BIFM)</th>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col0\" class=\"data row0 col0\" >391</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col1\" class=\"data row0 col1\" >2815</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col2\" class=\"data row0 col2\" >288</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col3\" class=\"data row0 col3\" >720</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col4\" class=\"data row0 col4\" >0.777494</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col5\" class=\"data row0 col5\" >131</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col6\" class=\"data row0 col6\" >7</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col7\" class=\"data row0 col7\" >218</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col8\" class=\"data row0 col8\" >11</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col9\" class=\"data row0 col9\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col10\" class=\"data row0 col10\" >9</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col11\" class=\"data row0 col11\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col12\" class=\"data row0 col12\" >23</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col13\" class=\"data row0 col13\" >4</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col14\" class=\"data row0 col14\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col15\" class=\"data row0 col15\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col16\" class=\"data row0 col16\" >16</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col17\" class=\"data row0 col17\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col18\" class=\"data row0 col18\" >206</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col19\" class=\"data row0 col19\" >20</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col20\" class=\"data row0 col20\" >47</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col21\" class=\"data row0 col21\" >4</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col22\" class=\"data row0 col22\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col23\" class=\"data row0 col23\" >8</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row0_col24\" class=\"data row0 col24\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122level0_row1\" class=\"row_heading level0 row1\" >Jemaah Islamiya (JI)</th>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col0\" class=\"data row1 col0\" >76</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col1\" class=\"data row1 col1\" >29</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col2\" class=\"data row1 col2\" >341</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col3\" class=\"data row1 col3\" >1195</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col4\" class=\"data row1 col4\" >0.763158</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col5\" class=\"data row1 col5\" >2</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col6\" class=\"data row1 col6\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col7\" class=\"data row1 col7\" >73</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col8\" class=\"data row1 col8\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col9\" class=\"data row1 col9\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col10\" class=\"data row1 col10\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col11\" class=\"data row1 col11\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col12\" class=\"data row1 col12\" >8</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col13\" class=\"data row1 col13\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col14\" class=\"data row1 col14\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col15\" class=\"data row1 col15\" >3</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col16\" class=\"data row1 col16\" >4</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col17\" class=\"data row1 col17\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col18\" class=\"data row1 col18\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col19\" class=\"data row1 col19\" >2</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col20\" class=\"data row1 col20\" >8</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col21\" class=\"data row1 col21\" >38</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col22\" class=\"data row1 col22\" >2</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col23\" class=\"data row1 col23\" >5</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row1_col24\" class=\"data row1 col24\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122level0_row2\" class=\"row_heading level0 row2\" >Magahat Militia</th>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col0\" class=\"data row2 col0\" >2</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col2\" class=\"data row2 col2\" >3</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col4\" class=\"data row2 col4\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col5\" class=\"data row2 col5\" >2</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col7\" class=\"data row2 col7\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col8\" class=\"data row2 col8\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col9\" class=\"data row2 col9\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col10\" class=\"data row2 col10\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col11\" class=\"data row2 col11\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col12\" class=\"data row2 col12\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col13\" class=\"data row2 col13\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col14\" class=\"data row2 col14\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col15\" class=\"data row2 col15\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col16\" class=\"data row2 col16\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col17\" class=\"data row2 col17\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col18\" class=\"data row2 col18\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col19\" class=\"data row2 col19\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col20\" class=\"data row2 col20\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col21\" class=\"data row2 col21\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col22\" class=\"data row2 col22\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col23\" class=\"data row2 col23\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row2_col24\" class=\"data row2 col24\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122level0_row3\" class=\"row_heading level0 row3\" >National Democratic Front-Bicol (NDF-Bicol)</th>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col1\" class=\"data row3 col1\" >3</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col2\" class=\"data row3 col2\" >2</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col3\" class=\"data row3 col3\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col4\" class=\"data row3 col4\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col5\" class=\"data row3 col5\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col7\" class=\"data row3 col7\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col8\" class=\"data row3 col8\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col9\" class=\"data row3 col9\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col10\" class=\"data row3 col10\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col11\" class=\"data row3 col11\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col12\" class=\"data row3 col12\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col13\" class=\"data row3 col13\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col14\" class=\"data row3 col14\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col15\" class=\"data row3 col15\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col16\" class=\"data row3 col16\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col17\" class=\"data row3 col17\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col18\" class=\"data row3 col18\" >2</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col19\" class=\"data row3 col19\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col20\" class=\"data row3 col20\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col21\" class=\"data row3 col21\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col22\" class=\"data row3 col22\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col23\" class=\"data row3 col23\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row3_col24\" class=\"data row3 col24\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122level0_row4\" class=\"row_heading level0 row4\" >Supporters of Muse Sudi Yalahow</th>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col0\" class=\"data row4 col0\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col2\" class=\"data row4 col2\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col3\" class=\"data row4 col3\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col4\" class=\"data row4 col4\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col5\" class=\"data row4 col5\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col6\" class=\"data row4 col6\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col7\" class=\"data row4 col7\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col8\" class=\"data row4 col8\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col9\" class=\"data row4 col9\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col10\" class=\"data row4 col10\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col11\" class=\"data row4 col11\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col12\" class=\"data row4 col12\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col13\" class=\"data row4 col13\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col14\" class=\"data row4 col14\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col15\" class=\"data row4 col15\" >1</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col16\" class=\"data row4 col16\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col17\" class=\"data row4 col17\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col18\" class=\"data row4 col18\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col19\" class=\"data row4 col19\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col20\" class=\"data row4 col20\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col21\" class=\"data row4 col21\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col22\" class=\"data row4 col22\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col23\" class=\"data row4 col23\" >0</td>\n",
       "                        <td id=\"T_cefc10d8_43d2_11ea_b9bc_acde48001122row4_col24\" class=\"data row4 col24\" >0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14a51d4a8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_feats = filtered_features[\n",
    "    filtered_features.index.isin(\n",
    "        list(clustered_df.index[clustered_df.cluster == cluster_id])\n",
    "    )\n",
    "]\n",
    "# show only non-zero columns\n",
    "features_nonzero = cluster_feats.loc[:, (cluster_feats != 0).any()]\n",
    "features_nonzero.style.background_gradient(cmap=\"RdYlGn_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of the isolated nodes in the cluster have features similar to those in the tight clique, e.g., in most cases they have high number of attacks, high success ratio, and attacks being focused mostly on bombings, and their targets are often the police. \n",
    "\n",
    "Note that there are terrorist groups that differ from the rest of the groups in the cluster in terms of their features. By taking a closer look we can observe that these terrorist groups are a part of a tight clique. For example, _Martyr al-Nimr Battalion_ has number of bombings equal to 0, but it is a part of a fully connected subgraph. \n",
    "\n",
    "Interestingly, _Al-Qaida in Saudi Arabia_ ends up in the same cluster as _Al-Qaida in Yemen_, though they are not connected directly in the network.    \n",
    "\n",
    "Thus we can observe that clustering on GraphSAGE embeddings combines groups based both on the underlying structure as well as features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = cluster_G.nodes()\n",
    "com_avg_coords = avg_coords[avg_coords.index.isin(list(nodes))]\n",
    "new_order = [1, 0]\n",
    "com_avg_coords = com_avg_coords[com_avg_coords.columns[new_order]]\n",
    "com_avg_coords.fillna(\n",
    "    com_avg_coords.mean(), inplace=True\n",
    ")  # fill missing values with the average\n",
    "pos = com_avg_coords.T.to_dict(\"list\")  # layout is based on the provided coordindates\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(22, 12))\n",
    "\n",
    "nx.draw_networkx_nodes(\n",
    "    cluster_G, pos, nodelist=nodes, with_labels=True, node_size=200, alpha=0.5\n",
    ")\n",
    "nx.draw_networkx_labels(cluster_G, pos, font_color=\"red\", font_size=50)\n",
    "nx.draw_networkx_edges(cluster_G, pos, edge_color=\"grey\")\n",
    "\n",
    "mplleaflet.display(fig=ax.figure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(**N.B.:** the above interactive plot will only appear after running the cell, and is not rendered in GitHub!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These groups are also very closely located, but in contrast with infomap, this is not the only thing that defines the clustering groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GSEC results\n",
    "\n",
    "What we can see from the results above is a somehow different picture from the infomap (note that by rerunning this notebook, the results might change due to the nature of the GraphSAGE predictions). Some clusters are identical to the infomap, showing that GSEC __capture__ the network structure. But some of the clusters differ - they are not necessary connected. By observing the feature table we can see that it has terrorist groups with __similar characteristics__. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this use case we demonstrated the conceptual differences of traditional community detection and unsupervised GraphSAGE embeddings clustering (GSEC) on a real dataset. We can observe that the traditional approach to the community detection via graph partitioning produces communities that are related to the graph structure only, while GSEC combines the features and the structure. However, in the latter case we should be very conscious of the network schema and the features, as these significantly affect both the clustering and the interpretation of the resulting clusters. \n",
    "\n",
    "For example, if we don't want the clusters where neither number of kills nor country play a role, we might want to exclude `nkills` from the feature set, and create a graph, where groups are connected only via the decade, and not via country. Then, the resulting groups would probably be related to the terrorist activities in time, and grouped by their similarity in their target and the types of attacks.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/community_detection/attacks_clustering_analysis.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/community_detection/attacks_clustering_analysis.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
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